First iteration of Plastic ID on local Machine

knitr::opts_chunk$set(warning = FALSE)
source("Raman_functions.R")
library(png)
library(bmp)
library(pixmap)
library(baseline)

## 
## Attaching package: 'baseline'

## The following object is masked from 'package:stats':
## 
##     getCall

library(hyperSpec)

## Loading required package: lattice

## Loading required package: grid

## Loading required package: ggplot2

## Loading required package: xml2

## Package hyperSpec, version 0.100.0
## 
## To get started, try
##    vignette ("hyperspec")
##    package?hyperSpec 
##    vignette (package = "hyperSpec")
## 
## If you use this package please cite it appropriately.
##    citation("hyperSpec")
## will give you the correct reference.
## 
## The project homepage is http://hyperspec.r-forge.r-project.org

library(prospectr)

## prospectr version 0.2.6 -- 'chicago'

## check the github repository at: https://github.com/l-ramirez-lopez/prospectr/

## 
## Attaching package: 'prospectr'

## The following object is masked from 'package:baseline':
## 
##     baseline

library(ggplot2)
library(magrittr)

## Warning: package 'magrittr' was built under R version 4.0.5

library(ggpubr)

## 
## Attaching package: 'ggpubr'

## The following object is masked from 'package:hyperSpec':
## 
##     mean_sd

library(signal)

## 
## Attaching package: 'signal'

## The following object is masked from 'package:prospectr':
## 
##     resample

## The following objects are masked from 'package:stats':
## 
##     filter, poly

library(OpenSpecy)

## This version of bslib is designed to work with shiny version 1.6.0 or higher.

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

## ✔ tibble  3.1.2     ✔ dplyr   1.0.6
## ✔ tidyr   1.1.3     ✔ stringr 1.4.0
## ✔ readr   1.4.0     ✔ forcats 0.5.0
## ✔ purrr   0.3.4

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::collapse()  masks hyperSpec::collapse()
## ✖ tidyr::extract()   masks magrittr::extract()
## ✖ dplyr::filter()    masks signal::filter(), stats::filter()
## ✖ dplyr::lag()       masks stats::lag()
## ✖ purrr::set_names() masks magrittr::set_names()

1 Introduction

The goal of this project is to not only enumerate total plastics but to also identify the and enumerate the plastic polymers polluting the Southern Ocean. This project’s goal is to identify the

read in .txt files

test2_10_8 <- read.delim(file = "ren4EB4_0__Time_0_test2_10_8.txt", header = F, sep = "\t")

#format table

colnames(test2_10_8) <-  c("Raman_shift", "Counts")
test2_10_8 <- as.data.frame(test2_10_8)

#repeat for each table
test_control <-  read.delim("renB232_0__Time_0_test_control.txt", header = F, sep = "\t")
colnames(test_control) <- c("Raman_shift", "Counts")
test_control <- as.data.frame(test_control)

pot_plastic <-  read.delim("ren79C7_0__Time_0_potentialPlastic_100ul.txt", header = F, sep = "\t")
colnames(pot_plastic) <- c("Raman_shift", "Counts")
pot_plastic <- as.data.frame(pot_plastic)

pot_plastic1 <-  read.delim("ren76AF_0__Time_0_potPla1_100ul.txt", header = F, sep = "\t")
colnames(pot_plastic1) <- c("Raman_shift", "Counts")
pot_plastic1 <- as.data.frame(pot_plastic1)

b <- list(test_control, pot_plastic,pot_plastic1, test2_10_8)
head(b)

## [[1]]
##      Raman_shift      Counts
## 1      1930.3320 13335.80664
## 2      1928.7832 13318.06445
## 3      1927.2324 13339.31250
## 4      1925.6816 13378.28613
## 5      1924.1309 13141.61231
## 6      1922.5801 13425.56445
## 7      1921.0273 13259.87598
## 8      1919.4746 13297.22461
## 9      1917.9238 13233.09766
## 10     1916.3711 13278.48633
## 11     1914.8164 13207.95410
## 12     1913.2637 13420.70508
## 13     1911.7109 13192.48828
## 14     1910.1562 13149.37012
## 15     1908.6016 13376.47070
## 16     1907.0469 13170.92676
## 17     1905.4922 13195.36328
## 18     1903.9355 13280.86523
## 19     1902.3809 13086.72754
## 20     1900.8242 13271.82422
## 21     1899.2676 13195.02734
## 22     1897.7109 13142.35254
## 23     1896.1543 13141.07715
## 24     1894.5977 13226.48144
## 25     1893.0391 13059.89453
## 26     1891.4805 13178.97363
## 27     1889.9219 13241.85742
## 28     1888.3633 13485.93750
## 29     1886.8047 13203.99609
## 30     1885.2441 13170.65137
## 31     1883.6855 13140.52637
## 32     1882.1250 13342.75586
## 33     1880.5645 13046.67578
## 34     1879.0039 13144.73047
## 35     1877.4414 13050.58691
## 36     1875.8809 13203.03125
## 37     1874.3184 13020.88379
## 38     1872.7559 13058.04981
## 39     1871.1934 13168.79590
## 40     1869.6309 13087.54785
## 41     1868.0664 13260.60938
## 42     1866.5039 13214.55176
## 43     1864.9395 13155.72656
## 44     1863.3750 13151.26465
## 45     1861.8105 13226.69531
## 46     1860.2461 12945.85938
## 47     1858.6797 13083.58984
## 48     1857.1152 13195.71289
## 49     1855.5488 13149.74121
## 50     1853.9824 12918.64453
## 51     1852.4160 13201.47461
## 52     1850.8496 13122.02930
## 53     1849.2812 13127.15527
## 54     1847.7129 13097.19629
## 55     1846.1465 13177.26074
## 56     1844.5781 13019.78418
## 57     1843.0078 12956.40137
## 58     1841.4395 13089.02344
## 59     1839.8711 12995.38769
## 60     1838.3008 13064.24023
## 61     1836.7305 12989.75781
## 62     1835.1602 13150.91309
## 63     1833.5898 12998.45703
## 64     1832.0176 13035.42383
## 65     1830.4473 12938.74023
## 66     1828.8750 12875.50000
## 67     1827.3027 12902.92969
## 68     1825.7305 12933.52637
## 69     1824.1582 12933.90918
## 70     1822.5840 12827.81738
## 71     1821.0117 12817.10156
## 72     1819.4375 12717.43555
## 73     1817.8633 12863.97461
## 74     1816.2891 12915.18164
## 75     1814.7129 12869.53320
## 76     1813.1387 12850.88379
## 77     1811.5625 12919.51660
## 78     1809.9863 12910.38379
## 79     1808.4102 12701.40723
## 80     1806.8340 12755.75586
## 81     1805.2578 12765.68945
## 82     1803.6797 12794.63965
## 83     1802.1016 12923.41406
## 84     1800.5254 12741.59473
## 85     1798.9473 12654.89648
## 86     1797.3672 12744.02637
## 87     1795.7891 12662.11426
## 88     1794.2090 12774.94922
## 89     1792.6309 12740.54688
## 90     1791.0508 12918.19141
## 91     1789.4707 12746.13965
## 92     1787.8887 12819.31738
## 93     1786.3086 12691.63769
## 94     1784.7266 12718.94727
## 95     1783.1445 12953.30859
## 96     1781.5625 12771.95215
## 97     1779.9805 12939.84277
## 98     1778.3984 12875.45801
## 99     1776.8145 12970.60742
## 100    1775.2324 13008.87500
## 101    1773.6484 12922.41602
## 102    1772.0645 13045.90332
## 103    1770.4805 13191.42773
## 104    1768.8945 13089.20898
## 105    1767.3105 13397.11816
## 106    1765.7246 13444.70606
## 107    1764.1387 13307.81348
## 108    1762.5527 13506.71777
## 109    1760.9668 13711.83984
## 110    1759.3789 13883.78711
## 111    1757.7930 13967.44824
## 112    1756.2051 14128.24219
## 113    1754.6172 14175.60059
## 114    1753.0293 14229.23438
## 115    1751.4414 14536.23926
## 116    1749.8516 14868.28711
## 117    1748.2637 14485.96387
## 118    1746.6738 14732.94727
## 119    1745.0840 14723.50293
## 120    1743.4941 14993.91113
## 121    1741.9023 14956.12207
## 122    1740.3125 14941.92090
## 123    1738.7207 14962.29199
## 124    1737.1289 14666.91504
## 125    1735.5371 14725.02734
## 126    1733.9453 14509.90625
## 127    1732.3535 14396.91504
## 128    1730.7598 14153.70410
## 129    1729.1680 14122.43359
## 130    1727.5742 14008.02832
## 131    1725.9805 13664.67285
## 132    1724.3848 13625.68457
## 133    1722.7910 13549.08594
## 134    1721.1953 13483.49219
## 135    1719.6016 13383.44824
## 136    1718.0059 13444.82910
## 137    1716.4082 13087.93359
## 138    1714.8125 13191.63574
## 139    1713.2168 13104.26562
## 140    1711.6191 13098.33691
## 141    1710.0215 13045.45606
## 142    1708.4238 13089.61523
## 143    1706.8262 12958.53418
## 144    1705.2285 13170.05566
## 145    1703.6289 13084.36328
## 146    1702.0312 12972.12891
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## 148    1698.8320 13241.64746
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## 158    1682.8164 15699.24121
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## 160    1679.6074 16534.30664
## 161    1678.0039 16667.92969
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## 163    1674.7949 17542.54883
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## 169    1665.1582 18688.55859
## 170    1663.5508 18739.01953
## 171    1661.9434 18693.03906
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## 173    1658.7285 18986.65039
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## 185    1639.4023 17514.80273
## 186    1637.7891 17123.52344
## 187    1636.1758 16932.34375
## 188    1634.5625 16578.54297
## 189    1632.9492 15913.50000
## 190    1631.3359 15518.24512
## 191    1629.7207 15149.48340
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## 193    1626.4922 14565.67090
## 194    1624.8750 14172.58398
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## 991     216.4004 17452.34375
## 992     214.4668 17646.12500
## 993     212.5312 17844.99023
## 994     210.5957 18079.93750
## 995     208.6602 18242.44922
## 996     206.7246 18336.44922
## 997     204.7871 18214.80859
## 998     202.8516 18227.45898
## 999     200.9141 18077.51172
## 1000    198.9766 17839.90234
## 1001    197.0371 17701.72070
## 1002    195.0996 17278.61328
## 1003    193.1602 17045.21680
## 1004    191.2207 16488.39844
## 1005    189.2812 16359.67090
## 1006    187.3398 16082.83789
## 1007    185.3984 15910.46582
## 1008    183.4570 15726.58301
## 1009    181.5156 15703.72168
## 1010    179.5742 15710.47754
## 1011    177.6309 15854.93262
## 1012    175.6875 15901.53906
## 1013    173.7441 16081.90527
## 1014    171.8008 16318.77734
## 1015    169.8574 16655.83398
## 1016    167.9121 17055.72656
## 1017    165.9668 17614.80469
## 1018    164.0215 18051.56836
## 1019    162.0742 18680.82617
## 1020    160.1289 19167.24414
## 1021    158.1816 19962.89648
## 1022    156.2344 20786.43750
## 1023    154.2852 21504.36328
## 1024    152.3379   514.55194
## 1025    150.3887    30.78923
## 1026    148.4395    14.10857
## 1027    146.4902    29.49314
## 1028    144.5391    11.53821
## 1029    142.5898    19.22605
## 1030    140.6387    17.94031
## 1031    138.6875   -15.37397
## 
## [[2]]
##      Raman_shift       Counts
## 1      1930.3320 2.730663e+05
## 2      1928.7832 2.736308e+05
## 3      1927.2324 2.737284e+05
## 4      1925.6816 2.738018e+05
## 5      1924.1309 2.729725e+05
## 6      1922.5801 2.725288e+05
## 7      1921.0273 2.724107e+05
## 8      1919.4746 2.727904e+05
## 9      1917.9238 2.723680e+05
## 10     1916.3711 2.716945e+05
## 11     1914.8164 2.713900e+05
## 12     1913.2637 2.717826e+05
## 13     1911.7109 2.718708e+05
## 14     1910.1562 2.710356e+05
## 15     1908.6016 2.709133e+05
## 16     1907.0469 2.721643e+05
## 17     1905.4922 2.713184e+05
## 18     1903.9355 2.708538e+05
## 19     1902.3809 2.701371e+05
## 20     1900.8242 2.700216e+05
## 21     1899.2676 2.704234e+05
## 22     1897.7109 2.700284e+05
## 23     1896.1543 2.693640e+05
## 24     1894.5977 2.694302e+05
## 25     1893.0391 2.693936e+05
## 26     1891.4805 2.694903e+05
## 27     1889.9219 2.685571e+05
## 28     1888.3633 2.684614e+05
## 29     1886.8047 2.681285e+05
## 30     1885.2441 2.679416e+05
## 31     1883.6855 2.678863e+05
## 32     1882.1250 2.683693e+05
## 33     1880.5645 2.675418e+05
## 34     1879.0039 2.671854e+05
## 35     1877.4414 2.667444e+05
## 36     1875.8809 2.664364e+05
## 37     1874.3184 2.664151e+05
## 38     1872.7559 2.651328e+05
## 39     1871.1934 2.654334e+05
## 40     1869.6309 2.658730e+05
## 41     1868.0664 2.657143e+05
## 42     1866.5039 2.652376e+05
## 43     1864.9395 2.657457e+05
## 44     1863.3750 2.651332e+05
## 45     1861.8105 2.647943e+05
## 46     1860.2461 2.640690e+05
## 47     1858.6797 2.639796e+05
## 48     1857.1152 2.635326e+05
## 49     1855.5488 2.636589e+05
## 50     1853.9824 2.634117e+05
## 51     1852.4160 2.633593e+05
## 52     1850.8496 2.616381e+05
## 53     1849.2812 2.621396e+05
## 54     1847.7129 2.626025e+05
## 55     1846.1465 2.620099e+05
## 56     1844.5781 2.615163e+05
## 57     1843.0078 2.621018e+05
## 58     1841.4395 2.610507e+05
## 59     1839.8711 2.605418e+05
## 60     1838.3008 2.607019e+05
## 61     1836.7305 2.607123e+05
## 62     1835.1602 2.601369e+05
## 63     1833.5898 2.602890e+05
## 64     1832.0176 2.597315e+05
## 65     1830.4473 2.593030e+05
## 66     1828.8750 2.591133e+05
## 67     1827.3027 2.587630e+05
## 68     1825.7305 2.588915e+05
## 69     1824.1582 2.581298e+05
## 70     1822.5840 2.579850e+05
## 71     1821.0117 2.576544e+05
## 72     1819.4375 2.567712e+05
## 73     1817.8633 2.570921e+05
## 74     1816.2891 2.576336e+05
## 75     1814.7129 2.569636e+05
## 76     1813.1387 2.551449e+05
## 77     1811.5625 2.561212e+05
## 78     1809.9863 2.563611e+05
## 79     1808.4102 2.565913e+05
## 80     1806.8340 2.550231e+05
## 81     1805.2578 2.549602e+05
## 82     1803.6797 2.550337e+05
## 83     1802.1016 2.538171e+05
## 84     1800.5254 2.541300e+05
## 85     1798.9473 2.537078e+05
## 86     1797.3672 2.533174e+05
## 87     1795.7891 2.537347e+05
## 88     1794.2090 2.528902e+05
## 89     1792.6309 2.528073e+05
## 90     1791.0508 2.537592e+05
## 91     1789.4707 2.527808e+05
## 92     1787.8887 2.523326e+05
## 93     1786.3086 2.505768e+05
## 94     1784.7266 2.525340e+05
## 95     1783.1445 2.511187e+05
## 96     1781.5625 2.518439e+05
## 97     1779.9805 2.511120e+05
## 98     1778.3984 2.516188e+05
## 99     1776.8145 2.513863e+05
## 100    1775.2324 2.513844e+05
## 101    1773.6484 2.507321e+05
## 102    1772.0645 2.501496e+05
## 103    1770.4805 2.499602e+05
## 104    1768.8945 2.499066e+05
## 105    1767.3105 2.514286e+05
## 106    1765.7246 2.494808e+05
## 107    1764.1387 2.499460e+05
## 108    1762.5527 2.506222e+05
## 109    1760.9668 2.485324e+05
## 110    1759.3789 2.490369e+05
## 111    1757.7930 2.488087e+05
## 112    1756.2051 2.487443e+05
## 113    1754.6172 2.487886e+05
## 114    1753.0293 2.481702e+05
## 115    1751.4414 2.481013e+05
## 116    1749.8516 2.488271e+05
## 117    1748.2637 2.473815e+05
## 118    1746.6738 2.483226e+05
## 119    1745.0840 2.473747e+05
## 120    1743.4941 2.476204e+05
## 121    1741.9023 2.472342e+05
## 122    1740.3125 2.471687e+05
## 123    1738.7207 2.464905e+05
## 124    1737.1289 2.468258e+05
## 125    1735.5371 2.460584e+05
## 126    1733.9453 2.454547e+05
## 127    1732.3535 2.453849e+05
## 128    1730.7598 2.453372e+05
## 129    1729.1680 2.450227e+05
## 130    1727.5742 2.459382e+05
## 131    1725.9805 2.449854e+05
## 132    1724.3848 2.447088e+05
## 133    1722.7910 2.448791e+05
## 134    1721.1953 2.450180e+05
## 135    1719.6016 2.443028e+05
## 136    1718.0059 2.443133e+05
## 137    1716.4082 2.439180e+05
## 138    1714.8125 2.440038e+05
## 139    1713.2168 2.430388e+05
## 140    1711.6191 2.432546e+05
## 141    1710.0215 2.432105e+05
## 142    1708.4238 2.437297e+05
## 143    1706.8262 2.433226e+05
## 144    1705.2285 2.437633e+05
## 145    1703.6289 2.432438e+05
## 146    1702.0312 2.427916e+05
## 147    1700.4316 2.420958e+05
## 148    1698.8320 2.428834e+05
## 149    1697.2305 2.428159e+05
## 150    1695.6309 2.410380e+05
## 151    1694.0293 2.419877e+05
## 152    1692.4297 2.420141e+05
## 153    1690.8281 2.407245e+05
## 154    1689.2266 2.410681e+05
## 155    1687.6230 2.414926e+05
## 156    1686.0215 2.417454e+05
## 157    1684.4180 2.408313e+05
## 158    1682.8164 2.401530e+05
## 159    1681.2129 2.406725e+05
## 160    1679.6074 2.416702e+05
## 161    1678.0039 2.409205e+05
## 162    1676.4004 2.406885e+05
## 163    1674.7949 2.406839e+05
## 164    1673.1895 2.414986e+05
## 165    1671.5840 2.403447e+05
## 166    1669.9785 2.403277e+05
## 167    1668.3730 2.413832e+05
## 168    1666.7656 2.403980e+05
## 169    1665.1582 2.405257e+05
## 170    1663.5508 2.408540e+05
## 171    1661.9434 2.413595e+05
## 172    1660.3359 2.408820e+05
## 173    1658.7285 2.417417e+05
## 174    1657.1191 2.418782e+05
## 175    1655.5098 2.409038e+05
## 176    1653.9004 2.409379e+05
## 177    1652.2910 2.422301e+05
## 178    1650.6816 2.418757e+05
## 179    1649.0723 2.427790e+05
## 180    1647.4609 2.431185e+05
## 181    1645.8496 2.428867e+05
## 182    1644.2383 2.428582e+05
## 183    1642.6270 2.440552e+05
## 184    1641.0156 2.446888e+05
## 185    1639.4023 2.437808e+05
## 186    1637.7891 2.446311e+05
## 187    1636.1758 2.452361e+05
## 188    1634.5625 2.459169e+05
## 189    1632.9492 2.465121e+05
## 190    1631.3359 2.456464e+05
## 191    1629.7207 2.469801e+05
## 192    1628.1055 2.485810e+05
## 193    1626.4922 2.490900e+05
## 194    1624.8750 2.492861e+05
## 195    1623.2598 2.492300e+05
## 196    1621.6445 2.498529e+05
## 197    1620.0273 2.510942e+05
## 198    1618.4102 2.507532e+05
## 199    1616.7930 2.514140e+05
## 200    1615.1758 2.520745e+05
## 201    1613.5586 2.531659e+05
## 202    1611.9395 2.532603e+05
## 203    1610.3223 2.530659e+05
## 204    1608.7031 2.537563e+05
## 205    1607.0840 2.539679e+05
## 206    1605.4629 2.547643e+05
## 207    1603.8438 2.541252e+05
## 208    1602.2227 2.547778e+05
## 209    1600.6035 2.557463e+05
## 210    1598.9824 2.567452e+05
## 211    1597.3613 2.559813e+05
## 212    1595.7383 2.561149e+05
## 213    1594.1172 2.565905e+05
## 214    1592.4941 2.561183e+05
## 215    1590.8711 2.560746e+05
## 216    1589.2480 2.556966e+05
## 217    1587.6250 2.565769e+05
## 218    1586.0020 2.565177e+05
## 219    1584.3770 2.571527e+05
## 220    1582.7539 2.563653e+05
## 221    1581.1289 2.564677e+05
## 222    1579.5039 2.559270e+05
## 223    1577.8789 2.559924e+05
## 224    1576.2520 2.547077e+05
## 225    1574.6270 2.552223e+05
## 226    1573.0000 2.557920e+05
## 227    1571.3730 2.545204e+05
## 228    1569.7461 2.548273e+05
## 229    1568.1172 2.545134e+05
## 230    1566.4902 2.537742e+05
## 231    1564.8613 2.536710e+05
## 232    1563.2344 2.531902e+05
## 233    1561.6055 2.531501e+05
## 234    1559.9746 2.528552e+05
## 235    1558.3457 2.516094e+05
## 236    1556.7148 2.527905e+05
## 237    1555.0859 2.509317e+05
## 238    1553.4551 2.512048e+05
## 239    1551.8242 2.498652e+05
## 240    1550.1934 2.498763e+05
## 241    1548.5605 2.492792e+05
## 242    1546.9277 2.487052e+05
## 243    1545.2969 2.484133e+05
## 244    1543.6641 2.483174e+05
## 245    1542.0312 2.475878e+05
## 246    1540.3965 2.482575e+05
## 247    1538.7637 2.464065e+05
## 248    1537.1289 2.457635e+05
## 249    1535.4941 2.464345e+05
## 250    1533.8594 2.443603e+05
## 251    1532.2246 2.446247e+05
## 252    1530.5898 2.435453e+05
## 253    1528.9531 2.444485e+05
## 254    1527.3164 2.433146e+05
## 255    1525.6797 2.424195e+05
## 256    1524.0430 2.420103e+05
## 257    1522.4062 2.412242e+05
## 258    1520.7695 2.406827e+05
## 259    1519.1309 2.415181e+05
## 260    1517.4922 2.399864e+05
## 261    1515.8535 2.392562e+05
## 262    1514.2148 2.387705e+05
## 263    1512.5762 2.388613e+05
## 264    1510.9355 2.382538e+05
## 265    1509.2949 2.376221e+05
## 266    1507.6543 2.379082e+05
## 267    1506.0137 2.370818e+05
## 268    1504.3730 2.366976e+05
## 269    1502.7324 2.358154e+05
## 270    1501.0898 2.364365e+05
## 271    1499.4473 2.355335e+05
## 272    1497.8047 2.352701e+05
## 273    1496.1621 2.342398e+05
## 274    1494.5195 2.345473e+05
## 275    1492.8750 2.337579e+05
## 276    1491.2324 2.342493e+05
## 277    1489.5879 2.332231e+05
## 278    1487.9434 2.326427e+05
## 279    1486.2969 2.327084e+05
## 280    1484.6523 2.321588e+05
## 281    1483.0059 2.318038e+05
## 282    1481.3613 2.312970e+05
## 283    1479.7148 2.317274e+05
## 284    1478.0664 2.308701e+05
## 285    1476.4199 2.309178e+05
## 286    1474.7734 2.305057e+05
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## 289    1469.8281 2.288988e+05
## 290    1468.1797 2.289302e+05
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## 292    1464.8809 2.291898e+05
## 293    1463.2305 2.283982e+05
## 294    1461.5801 2.287552e+05
## 295    1459.9297 2.277566e+05
## 296    1458.2793 2.279607e+05
## 297    1456.6270 2.270398e+05
## 298    1454.9766 2.273528e+05
## 299    1453.3242 2.272451e+05
## 300    1451.6719 2.267894e+05
## 301    1450.0195 2.258831e+05
## 302    1448.3652 2.259360e+05
## 303    1446.7129 2.249818e+05
## 304    1445.0586 2.254248e+05
## 305    1443.4043 2.257830e+05
## 306    1441.7500 2.241223e+05
## 307    1440.0938 2.246724e+05
## 308    1438.4395 2.244324e+05
## 309    1436.7832 2.240701e+05
## 310    1435.1270 2.238242e+05
## 311    1433.4707 2.231045e+05
## 312    1431.8145 2.229418e+05
## 313    1430.1582 2.228034e+05
## 314    1428.5000 2.230150e+05
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## 319    1420.2090 2.221847e+05
## 320    1418.5488 2.219592e+05
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## 897     396.2539 7.748495e+04
## 898     394.3613 7.688045e+04
## 899     392.4668 7.717285e+04
## 900     390.5742 7.676673e+04
## 901     388.6797 7.687952e+04
## 902     386.7852 7.655679e+04
## 903     384.8906 7.600466e+04
## 904     382.9941 7.637738e+04
## 905     381.0996 7.583477e+04
## 906     379.2031 7.574725e+04
## 907     377.3066 7.576257e+04
## 908     375.4082 7.574495e+04
## 909     373.5117 7.571415e+04
## 910     371.6133 7.538043e+04
## 911     369.7148 7.516143e+04
## 912     367.8145 7.527560e+04
## 913     365.9160 7.499750e+04
## 914     364.0156 7.473268e+04
## 915     362.1152 7.481662e+04
## 916     360.2148 7.469796e+04
## 917     358.3145 7.498830e+04
## 918     356.4121 7.482102e+04
## 919     354.5098 7.476422e+04
## 920     352.6074 7.377053e+04
## 921     350.7051 7.379939e+04
## 922     348.8008 7.355110e+04
## 923     346.8965 7.410525e+04
## 924     344.9922 7.366008e+04
## 925     343.0879 7.368238e+04
## 926     341.1836 7.343827e+04
## 927     339.2773 7.318773e+04
## 928     337.3711 7.339506e+04
## 929     335.4648 7.332036e+04
## 930     333.5586 7.263345e+04
## 931     331.6504 7.260746e+04
## 932     329.7422 7.270334e+04
## 933     327.8340 7.209828e+04
## 934     325.9258 7.243000e+04
## 935     324.0176 7.217492e+04
## 936     322.1074 7.165550e+04
## 937     320.1973 7.174102e+04
## 938     318.2871 7.161059e+04
## 939     316.3750 7.186486e+04
## 940     314.4648 7.133945e+04
## 941     312.5527 7.142235e+04
## 942     310.6406 7.129996e+04
## 943     308.7285 7.125736e+04
## 944     306.8145 7.094559e+04
## 945     304.9004 7.071103e+04
## 946     302.9863 7.024670e+04
## 947     301.0723 7.069539e+04
## 948     299.1582 7.040884e+04
## 949     297.2422 7.043438e+04
## 950     295.3262 7.019370e+04
## 951     293.4102 7.014230e+04
## 952     291.4941 6.994875e+04
## 953     289.5762 6.973573e+04
## 954     287.6582 6.925685e+04
## 955     285.7402 6.939994e+04
## 956     283.8223 6.935660e+04
## 957     281.9043 6.944749e+04
## 958     279.9844 6.890659e+04
## 959     278.0645 6.876574e+04
## 960     276.1445 6.885019e+04
## 961     274.2246 6.858836e+04
## 962     272.3027 6.861816e+04
## 963     270.3809 6.898622e+04
## 964     268.4590 6.829790e+04
## 965     266.5371 6.779455e+04
## 966     264.6133 6.789212e+04
## 967     262.6914 6.742420e+04
## 968     260.7676 6.756080e+04
## 969     258.8438 6.759599e+04
## 970     256.9180 6.738177e+04
## 971     254.9941 6.742348e+04
## 972     253.0684 6.745998e+04
## 973     251.1426 6.677863e+04
## 974     249.2148 6.665045e+04
## 975     247.2891 6.658720e+04
## 976     245.3613 6.675100e+04
## 977     243.4336 6.691861e+04
## 978     241.5059 6.670235e+04
## 979     239.5781 6.600656e+04
## 980     237.6484 6.632975e+04
## 981     235.7188 6.648565e+04
## 982     233.7891 6.619586e+04
## 983     231.8594 6.606162e+04
## 984     229.9277 6.584845e+04
## 985     227.9961 6.565221e+04
## 986     226.0645 6.539912e+04
## 987     224.1328 6.560420e+04
## 988     222.2012 6.533313e+04
## 989     220.2676 6.541009e+04
## 990     218.3340 6.488963e+04
## 991     216.4004 6.490591e+04
## 992     214.4668 6.495966e+04
## 993     212.5312 6.497463e+04
## 994     210.5957 6.462915e+04
## 995     208.6602 6.447629e+04
## 996     206.7246 6.457788e+04
## 997     204.7871 6.420433e+04
## 998     202.8516 6.431497e+04
## 999     200.9141 6.374423e+04
## 1000    198.9766 6.375688e+04
## 1001    197.0371 6.371923e+04
## 1002    195.0996 6.365581e+04
## 1003    193.1602 6.378838e+04
## 1004    191.2207 6.357288e+04
## 1005    189.2812 6.361135e+04
## 1006    187.3398 6.328519e+04
## 1007    185.3984 6.329150e+04
## 1008    183.4570 6.323472e+04
## 1009    181.5156 6.302218e+04
## 1010    179.5742 6.290498e+04
## 1011    177.6309 6.266817e+04
## 1012    175.6875 6.266434e+04
## 1013    173.7441 6.252030e+04
## 1014    171.8008 6.236862e+04
## 1015    169.8574 6.240473e+04
## 1016    167.9121 6.213617e+04
## 1017    165.9668 6.206822e+04
## 1018    164.0215 6.212366e+04
## 1019    162.0742 6.211227e+04
## 1020    160.1289 6.165010e+04
## 1021    158.1816 6.180576e+04
## 1022    156.2344 6.148251e+04
## 1023    154.2852 6.117225e+04
## 1024    152.3379 1.434586e+03
## 1025    150.3887 6.157846e+01
## 1026    148.4395 4.232571e+01
## 1027    146.4902 2.436390e+01
## 1028    144.5391 4.230678e+01
## 1029    142.5898 4.357906e+01
## 1030    140.6387 2.947336e+01
## 1031    138.6875 2.562329e+00
## 
## [[3]]
##      Raman_shift       Counts
## 1      1930.3320 222753.78125
## 2      1928.7832 223002.17188
## 3      1927.2324 223275.45312
## 4      1925.6816 224243.48438
## 5      1924.1309 223036.67188
## 6      1922.5801 223419.37500
## 7      1921.0273 222583.85938
## 8      1919.4746 222280.29688
## 9      1917.9238 222933.48438
## 10     1916.3711 221828.15625
## 11     1914.8164 222205.90625
## 12     1913.2637 221514.85938
## 13     1911.7109 222188.59375
## 14     1910.1562 221388.48438
## 15     1908.6016 221394.50000
## 16     1907.0469 221784.82812
## 17     1905.4922 220859.96875
## 18     1903.9355 221817.60938
## 19     1902.3809 220955.82812
## 20     1900.8242 221074.43750
## 21     1899.2676 221164.07812
## 22     1897.7109 220807.26562
## 23     1896.1543 219519.40625
## 24     1894.5977 219970.51562
## 25     1893.0391 220427.84375
## 26     1891.4805 220410.07812
## 27     1889.9219 219877.39062
## 28     1888.3633 219723.43750
## 29     1886.8047 219513.42188
## 30     1885.2441 219388.48438
## 31     1883.6855 218151.32812
## 32     1882.1250 219419.18750
## 33     1880.5645 218475.73438
## 34     1879.0039 218705.12500
## 35     1877.4414 218449.20312
## 36     1875.8809 218343.90625
## 37     1874.3184 218529.95312
## 38     1872.7559 217712.57812
## 39     1871.1934 217732.31250
## 40     1869.6309 218025.56250
## 41     1868.0664 218026.03125
## 42     1866.5039 216992.04688
## 43     1864.9395 216661.85938
## 44     1863.3750 216434.10938
## 45     1861.8105 217021.34375
## 46     1860.2461 216859.10938
## 47     1858.6797 215777.01562
## 48     1857.1152 216036.68750
## 49     1855.5488 216008.87500
## 50     1853.9824 215570.90625
## 51     1852.4160 216049.07812
## 52     1850.8496 215721.37500
## 53     1849.2812 215108.29688
## 54     1847.7129 214241.93750
## 55     1846.1465 215022.92188
## 56     1844.5781 215059.15625
## 57     1843.0078 214354.34375
## 58     1841.4395 214081.62500
## 59     1839.8711 213777.15625
## 60     1838.3008 214527.14062
## 61     1836.7305 214412.20312
## 62     1835.1602 214236.82812
## 63     1833.5898 213779.81250
## 64     1832.0176 212764.46875
## 65     1830.4473 212580.01562
## 66     1828.8750 213229.10938
## 67     1827.3027 212779.07812
## 68     1825.7305 212281.54688
## 69     1824.1582 212102.12500
## 70     1822.5840 211949.81250
## 71     1821.0117 211049.21875
## 72     1819.4375 210789.18750
## 73     1817.8633 211080.34375
## 74     1816.2891 210685.45312
## 75     1814.7129 209914.51562
## 76     1813.1387 209939.03125
## 77     1811.5625 210293.60938
## 78     1809.9863 209775.48438
## 79     1808.4102 209577.98438
## 80     1806.8340 209356.78125
## 81     1805.2578 209218.10938
## 82     1803.6797 209474.17188
## 83     1802.1016 209326.03125
## 84     1800.5254 207991.25000
## 85     1798.9473 208312.35938
## 86     1797.3672 207955.51562
## 87     1795.7891 208571.01562
## 88     1794.2090 208165.18750
## 89     1792.6309 207675.65625
## 90     1791.0508 208433.21875
## 91     1789.4707 207165.50000
## 92     1787.8887 206799.87500
## 93     1786.3086 206726.93750
## 94     1784.7266 207033.46875
## 95     1783.1445 207115.42188
## 96     1781.5625 206614.20312
## 97     1779.9805 206437.09375
## 98     1778.3984 206585.46875
## 99     1776.8145 206868.01562
## 100    1775.2324 205972.53125
## 101    1773.6484 206019.87500
## 102    1772.0645 206217.12500
## 103    1770.4805 205394.95312
## 104    1768.8945 205469.18750
## 105    1767.3105 205691.67188
## 106    1765.7246 204794.43750
## 107    1764.1387 204711.09375
## 108    1762.5527 205223.60938
## 109    1760.9668 204218.31250
## 110    1759.3789 204059.51562
## 111    1757.7930 204288.29688
## 112    1756.2051 204523.28125
## 113    1754.6172 204156.67188
## 114    1753.0293 204265.62500
## 115    1751.4414 203211.45312
## 116    1749.8516 203910.64062
## 117    1748.2637 203089.81250
## 118    1746.6738 203515.04688
## 119    1745.0840 202851.90625
## 120    1743.4941 202987.71875
## 121    1741.9023 202505.73438
## 122    1740.3125 203125.57812
## 123    1738.7207 202488.21875
## 124    1737.1289 202111.89062
## 125    1735.5371 202194.29688
## 126    1733.9453 201990.89062
## 127    1732.3535 201657.28125
## 128    1730.7598 201722.46875
## 129    1729.1680 201406.31250
## 130    1727.5742 201476.20312
## 131    1725.9805 200427.75000
## 132    1724.3848 200776.95312
## 133    1722.7910 200748.15625
## 134    1721.1953 200443.50000
## 135    1719.6016 200088.82812
## 136    1718.0059 200483.21875
## 137    1716.4082 200034.68750
## 138    1714.8125 200156.40625
## 139    1713.2168 199910.10938
## 140    1711.6191 199479.20312
## 141    1710.0215 200918.79688
## 142    1708.4238 199550.51562
## 143    1706.8262 199410.92188
## 144    1705.2285 198841.25000
## 145    1703.6289 199358.65625
## 146    1702.0312 199422.43750
## 147    1700.4316 198624.89062
## 148    1698.8320 197896.45312
## 149    1697.2305 198540.15625
## 150    1695.6309 197827.64062
## 151    1694.0293 197671.43750
## 152    1692.4297 198121.14062
## 153    1690.8281 197076.64062
## 154    1689.2266 197474.76562
## 155    1687.6230 196977.00000
## 156    1686.0215 197524.73438
## 157    1684.4180 197211.21875
## 158    1682.8164 196568.78125
## 159    1681.2129 196600.17188
## 160    1679.6074 196580.07812
## 161    1678.0039 196973.00000
## 162    1676.4004 196826.62500
## 163    1674.7949 196675.64062
## 164    1673.1895 197361.07812
## 165    1671.5840 196911.04688
## 166    1669.9785 196725.85938
## 167    1668.3730 195818.54688
## 168    1666.7656 196433.53125
## 169    1665.1582 195994.93750
## 170    1663.5508 195996.75000
## 171    1661.9434 196393.54688
## 172    1660.3359 196661.12500
## 173    1658.7285 196096.93750
## 174    1657.1191 196706.35938
## 175    1655.5098 196465.57812
## 176    1653.9004 196148.76562
## 177    1652.2910 196565.18750
## 178    1650.6816 196574.57812
## 179    1649.0723 197416.09375
## 180    1647.4609 196767.15625
## 181    1645.8496 196635.25000
## 182    1644.2383 196964.18750
## 183    1642.6270 197266.62500
## 184    1641.0156 197556.51562
## 185    1639.4023 197357.84375
## 186    1637.7891 198128.12500
## 187    1636.1758 197946.46875
## 188    1634.5625 198119.70312
## 189    1632.9492 198027.95312
## 190    1631.3359 198246.01562
## 191    1629.7207 198510.42188
## 192    1628.1055 198575.00000
## 193    1626.4922 199795.79688
## 194    1624.8750 199199.25000
## 195    1623.2598 199600.89062
## 196    1621.6445 199612.54688
## 197    1620.0273 200315.50000
## 198    1618.4102 200634.68750
## 199    1616.7930 200332.26562
## 200    1615.1758 200711.56250
## 201    1613.5586 200407.68750
## 202    1611.9395 200680.20312
## 203    1610.3223 201437.57812
## 204    1608.7031 202330.53125
## 205    1607.0840 201827.35938
## 206    1605.4629 202420.37500
## 207    1603.8438 201650.39062
## 208    1602.2227 202107.37500
## 209    1600.6035 202670.60938
## 210    1598.9824 202297.40625
## 211    1597.3613 203318.29688
## 212    1595.7383 202152.70312
## 213    1594.1172 202425.62500
## 214    1592.4941 202552.04688
## 215    1590.8711 202270.10938
## 216    1589.2480 202846.26562
## 217    1587.6250 202850.81250
## 218    1586.0020 201683.65625
## 219    1584.3770 202467.34375
## 220    1582.7539 201927.12500
## 221    1581.1289 202044.14062
## 222    1579.5039 201922.65625
## 223    1577.8789 201456.70312
## 224    1576.2520 201679.81250
## 225    1574.6270 201641.48438
## 226    1573.0000 201312.64062
## 227    1571.3730 200867.14062
## 228    1569.7461 201139.31250
## 229    1568.1172 200196.32812
## 230    1566.4902 200239.68750
## 231    1564.8613 199865.37500
## 232    1563.2344 199730.68750
## 233    1561.6055 198995.95312
## 234    1559.9746 198987.31250
## 235    1558.3457 198885.07812
## 236    1556.7148 198942.39062
## 237    1555.0859 198479.82812
## 238    1553.4551 197773.68750
## 239    1551.8242 197405.04688
## 240    1550.1934 197036.56250
## 241    1548.5605 196652.89062
## 242    1546.9277 196241.79688
## 243    1545.2969 196235.18750
## 244    1543.6641 196369.43750
## 245    1542.0312 195698.39062
## 246    1540.3965 195903.07812
## 247    1538.7637 194741.20312
## 248    1537.1289 194274.37500
## 249    1535.4941 194304.82812
## 250    1533.8594 193645.57812
## 251    1532.2246 193322.95312
## 252    1530.5898 193399.42188
## 253    1528.9531 192907.32812
## 254    1527.3164 192644.62500
## 255    1525.6797 192181.90625
## 256    1524.0430 192114.93750
## 257    1522.4062 191828.09375
## 258    1520.7695 191112.39062
## 259    1519.1309 191379.90625
## 260    1517.4922 190296.82812
## 261    1515.8535 190527.76562
## 262    1514.2148 189562.76562
## 263    1512.5762 189177.64062
## 264    1510.9355 189000.01562
## 265    1509.2949 188990.14062
## 266    1507.6543 188651.07812
## 267    1506.0137 188359.37500
## 268    1504.3730 187050.18750
## 269    1502.7324 188420.54688
## 270    1501.0898 187425.54688
## 271    1499.4473 187117.56250
## 272    1497.8047 187010.60938
## 273    1496.1621 186276.71875
## 274    1494.5195 186760.26562
## 275    1492.8750 186201.65625
## 276    1491.2324 187217.23438
## 277    1489.5879 185992.79688
## 278    1487.9434 185997.10938
## 279    1486.2969 185533.31250
## 280    1484.6523 184898.00000
## 281    1483.0059 185690.92188
## 282    1481.3613 184645.76562
## 283    1479.7148 184211.45312
## 284    1478.0664 184023.25000
## 285    1476.4199 184354.20312
## 286    1474.7734 184247.98438
## 287    1473.1250 183833.79688
## 288    1471.4766 183403.09375
## 289    1469.8281 182678.34375
## 290    1468.1797 183783.95312
## 291    1466.5293 183438.39062
## 292    1464.8809 183462.73438
## 293    1463.2305 182349.14062
## 294    1461.5801 182147.98438
## 295    1459.9297 181904.48438
## 296    1458.2793 181850.34375
## 297    1456.6270 181276.95312
## 298    1454.9766 181275.93750
## 299    1453.3242 180568.20312
## 300    1451.6719 181254.18750
## 301    1450.0195 180973.28125
## 302    1448.3652 180247.89062
## 303    1446.7129 180009.64062
## 304    1445.0586 180037.53125
## 305    1443.4043 179722.31250
## 306    1441.7500 179973.85938
## 307    1440.0938 179711.64062
## 308    1438.4395 178905.81250
## 309    1436.7832 179330.96875
## 310    1435.1270 179046.37500
## 311    1433.4707 178675.85938
## 312    1431.8145 178148.54688
## 313    1430.1582 178306.40625
## 314    1428.5000 177984.51562
## 315    1426.8418 178249.39062
## 316    1425.1855 177725.57812
## 317    1423.5254 178652.12500
## 318    1421.8672 177117.15625
## 319    1420.2090 177416.59375
## 320    1418.5488 176717.09375
## 321    1416.8887 176932.10938
## 322    1415.2285 176943.73438
## 323    1413.5684 176265.82812
## 324    1411.9062 176133.06250
## 325    1410.2461 176969.46875
## 326    1408.5840 175599.87500
## 327    1406.9219 175381.56250
## 328    1405.2598 175713.75000
## 329    1403.5977 175152.65625
## 330    1401.9336 175604.98438
## 331    1400.2695 175588.18750
## 332    1398.6074 174790.04688
## 333    1396.9434 175060.32812
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## 335    1393.6133 173519.29688
## 336    1391.9473 174313.57812
## 337    1390.2812 174541.65625
## 338    1388.6152 174005.79688
## 339    1386.9492 173951.75000
## 340    1385.2832 174239.70312
## 341    1383.6152 173215.32812
## 342    1381.9492 173512.28125
## 343    1380.2812 173296.43750
## 344    1378.6133 173314.46875
## 345    1376.9453 172963.87500
## 346    1375.2754 173004.40625
## 347    1373.6055 173257.59375
## 348    1371.9375 172544.82812
## 349    1370.2676 172905.75000
## 350    1368.5957 172558.54688
## 351    1366.9258 172073.79688
## 352    1365.2539 171418.70312
## 353    1363.5840 172023.31250
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## 356    1358.5664 171247.96875
## 357    1356.8945 171382.76562
## 358    1355.2207 170966.14062
## 359    1353.5469 170714.01562
## 360    1351.8730 170977.23438
## 361    1350.1992 170611.68750
## 362    1348.5254 170047.73438
## 363    1346.8496 170437.75000
## 364    1345.1738 170679.85938
## 365    1343.4980 169582.10938
## 366    1341.8223 169651.25000
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## 370    1335.1152 168521.98438
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## 888     413.2695  70056.72656
## 889     411.3809  69951.69531
## 890     409.4902  69869.21094
## 891     407.6016  69799.98438
## 892     405.7109  69636.89062
## 893     403.8203  69504.28125
## 894     401.9297  69390.23438
## 895     400.0391  69565.70312
## 896     398.1465  69164.95312
## 897     396.2539  69249.23438
## 898     394.3613  69211.96094
## 899     392.4668  68911.92188
## 900     390.5742  68355.91406
## 901     388.6797  68821.74219
## 902     386.7852  68161.76562
## 903     384.8906  68205.25781
## 904     382.9941  68348.96875
## 905     381.0996  68401.62500
## 906     379.2031  68435.79688
## 907     377.3066  67887.39062
## 908     375.4082  68187.84375
## 909     373.5117  67973.04688
## 910     371.6133  67838.70312
## 911     369.7148  67512.15625
## 912     367.8145  67313.46875
## 913     365.9160  67100.39062
## 914     364.0156  67363.76562
## 915     362.1152  67058.68750
## 916     360.2148  67168.05469
## 917     358.3145  66927.59375
## 918     356.4121  66951.49219
## 919     354.5098  66805.82812
## 920     352.6074  66896.75781
## 921     350.7051  66371.50000
## 922     348.8008  66042.18750
## 923     346.8965  66221.20312
## 924     344.9922  66285.92969
## 925     343.0879  66187.86719
## 926     341.1836  65704.06250
## 927     339.2773  66040.42969
## 928     337.3711  65636.89062
## 929     335.4648  65373.84375
## 930     333.5586  65278.73047
## 931     331.6504  65348.80859
## 932     329.7422  65080.77344
## 933     327.8340  64896.70703
## 934     325.9258  64704.87109
## 935     324.0176  64740.96875
## 936     322.1074  64339.78906
## 937     320.1973  64611.55469
## 938     318.2871  64592.69141
## 939     316.3750  64517.58203
## 940     314.4648  64272.47266
## 941     312.5527  63960.78516
## 942     310.6406  64139.50391
## 943     308.7285  63946.93359
## 944     306.8145  63787.12500
## 945     304.9004  63691.40234
## 946     302.9863  63271.80078
## 947     301.0723  63282.01953
## 948     299.1582  63125.12500
## 949     297.2422  63012.67969
## 950     295.3262  63286.55469
## 951     293.4102  62721.41016
## 952     291.4941  62876.53125
## 953     289.5762  63154.16016
## 954     287.6582  62678.09375
## 955     285.7402  62780.98438
## 956     283.8223  62756.11719
## 957     281.9043  62858.94141
## 958     279.9844  62495.40625
## 959     278.0645  62301.32812
## 960     276.1445  61991.46875
## 961     274.2246  62134.73047
## 962     272.3027  61877.10938
## 963     270.3809  61453.07031
## 964     268.4590  61674.40625
## 965     266.5371  61417.07422
## 966     264.6133  61624.01562
## 967     262.6914  60949.55469
## 968     260.7676  60700.36328
## 969     258.8438  61451.72656
## 970     256.9180  61002.49609
## 971     254.9941  61117.09766
## 972     253.0684  60722.68359
## 973     251.1426  60691.91797
## 974     249.2148  60454.81641
## 975     247.2891  60564.25391
## 976     245.3613  60486.84766
## 977     243.4336  60307.02344
## 978     241.5059  60044.30078
## 979     239.5781  60088.92578
## 980     237.6484  60075.21094
## 981     235.7188  59982.46484
## 982     233.7891  59837.94531
## 983     231.8594  59689.60938
## 984     229.9277  59396.32422
## 985     227.9961  59754.32422
## 986     226.0645  59339.50000
## 987     224.1328  59150.01562
## 988     222.2012  59494.89453
## 989     220.2676  59459.37109
## 990     218.3340  58919.57422
## 991     216.4004  59001.84766
## 992     214.4668  59033.67578
## 993     212.5312  58546.03906
## 994     210.5957  58583.12891
## 995     208.6602  58607.28906
## 996     206.7246  58321.52734
## 997     204.7871  58242.46094
## 998     202.8516  57955.62891
## 999     200.9141  57987.66016
## 1000    198.9766  58011.93750
## 1001    197.0371  57929.15234
## 1002    195.0996  57927.64844
## 1003    193.1602  57706.97656
## 1004    191.2207  57273.72656
## 1005    189.2812  57460.52344
## 1006    187.3398  57514.53516
## 1007    185.3984  57153.76562
## 1008    183.4570  57166.61719
## 1009    181.5156  57312.08203
## 1010    179.5742  57088.05469
## 1011    177.6309  57062.31641
## 1012    175.6875  56682.76562
## 1013    173.7441  56735.60156
## 1014    171.8008  56828.28516
## 1015    169.8574  56351.33594
## 1016    167.9121  56333.51953
## 1017    165.9668  56615.16016
## 1018    164.0215  56142.44531
## 1019    162.0742  56249.30469
## 1020    160.1289  55870.64062
## 1021    158.1816  56086.66406
## 1022    156.2344  55929.01953
## 1023    154.2852  56011.45703
## 1024    152.3379   1319.10071
## 1025    150.3887     50.03250
## 1026    148.4395     32.06493
## 1027    146.4902     24.36390
## 1028    144.5391     25.64047
## 1029    142.5898     30.76169
## 1030    140.6387     32.03627
## 1031    138.6875     11.53048
## 
## [[4]]
##      Raman_shift      Counts
## 1      1930.3320 14582.61133
## 2      1928.7832 14480.99609
## 3      1927.2324 14600.35449
## 4      1925.6816 14500.39844
## 5      1924.1309 14722.86133
## 6      1922.5801 14561.68457
## 7      1921.0273 14550.41797
## 8      1919.4746 14516.60644
## 9      1917.9238 14651.90723
## 10     1916.3711 14519.87207
## 11     1914.8164 14632.57715
## 12     1913.2637 14614.87305
## 13     1911.7109 14487.76269
## 14     1910.1562 14510.31543
## 15     1908.6016 14492.65039
## 16     1907.0469 14526.45019
## 17     1905.4922 14613.28613
## 18     1903.9355 14362.55762
## 19     1902.3809 14595.62207
## 20     1900.8242 14467.10742
## 21     1899.2676 14439.84082
## 22     1897.7109 14427.04004
## 23     1896.1543 14586.03418
## 24     1894.5977 14451.21582
## 25     1893.0391 14513.84668
## 26     1891.4805 14509.06445
## 27     1889.9219 14193.08887
## 28     1888.3633 14510.73438
## 29     1886.8047 14396.92578
## 30     1885.2441 14295.99121
## 31     1883.6855 14283.25000
## 32     1882.1250 14534.90234
## 33     1880.5645 14432.40430
## 34     1879.0039 14461.28516
## 35     1877.4414 14490.15430
## 36     1875.8809 14402.14258
## 37     1874.3184 14338.17773
## 38     1872.7559 14312.64648
## 39     1871.1934 14263.12891
## 40     1869.6309 14466.36328
## 41     1868.0664 14316.08398
## 42     1866.5039 14469.65430
## 43     1864.9395 14372.19141
## 44     1863.3750 14295.54785
## 45     1861.8105 14476.18457
## 46     1860.2461 14284.53516
## 47     1858.6797 14398.01758
## 48     1857.1152 14452.37109
## 49     1855.5488 14275.21973
## 50     1853.9824 14356.70996
## 51     1852.4160 14458.90918
## 52     1850.8496 14269.10938
## 53     1849.2812 14360.11914
## 54     1847.7129 14203.91211
## 55     1846.1465 14234.31055
## 56     1844.5781 14226.44141
## 57     1843.0078 14260.00977
## 58     1841.4395 14231.42578
## 59     1839.8711 14304.80273
## 60     1838.3008 14328.77832
## 61     1836.7305 14233.32227
## 62     1835.1602 14325.75586
## 63     1833.5898 14238.29981
## 64     1832.0176 14354.56055
## 65     1830.4473 14166.91113
## 66     1828.8750 14162.25488
## 67     1827.3027 14241.88281
## 68     1825.7305 14238.80273
## 69     1824.1582 14068.82519
## 70     1822.5840 14100.74707
## 71     1821.0117 14057.97949
## 72     1819.4375 14161.37109
## 73     1817.8633 14045.55469
## 74     1816.2891 14225.11914
## 75     1814.7129 13972.81738
## 76     1813.1387 13857.11621
## 77     1811.5625 14154.02637
## 78     1809.9863 14001.84863
## 79     1808.4102 14244.67285
## 80     1806.8340 14035.45410
## 81     1805.2578 13969.01269
## 82     1803.6797 14181.57031
## 83     1802.1016 13910.69629
## 84     1800.5254 14002.76269
## 85     1798.9473 13979.15430
## 86     1797.3672 13941.30273
## 87     1795.7891 14150.47168
## 88     1794.2090 14156.92383
## 89     1792.6309 14003.52246
## 90     1791.0508 13987.85059
## 91     1789.4707 14060.77832
## 92     1787.8887 14064.08008
## 93     1786.3086 14056.31250
## 94     1784.7266 14005.86133
## 95     1783.1445 14059.75391
## 96     1781.5625 14031.44727
## 97     1779.9805 14090.05078
## 98     1778.3984 14178.64160
## 99     1776.8145 14065.04688
## 100    1775.2324 13997.29102
## 101    1773.6484 14316.31934
## 102    1772.0645 14205.92481
## 103    1770.4805 14300.70703
## 104    1768.8945 14491.68066
## 105    1767.3105 14583.20703
## 106    1765.7246 14658.92383
## 107    1764.1387 14740.91406
## 108    1762.5527 14848.08984
## 109    1760.9668 14956.79394
## 110    1759.3789 15145.80566
## 111    1757.7930 15271.72754
## 112    1756.2051 15292.07812
## 113    1754.6172 15342.33594
## 114    1753.0293 15696.39356
## 115    1751.4414 15722.93457
## 116    1749.8516 15689.66992
## 117    1748.2637 15903.41113
## 118    1746.6738 16027.40820
## 119    1745.0840 16027.12207
## 120    1743.4941 16176.19336
## 121    1741.9023 15954.24512
## 122    1740.3125 16125.26856
## 123    1738.7207 16033.83301
## 124    1737.1289 15845.04394
## 125    1735.5371 15761.55957
## 126    1733.9453 15813.14453
## 127    1732.3535 15552.31055
## 128    1730.7598 15415.57812
## 129    1729.1680 15206.72266
## 130    1727.5742 15241.12402
## 131    1725.9805 15059.05859
## 132    1724.3848 14966.45606
## 133    1722.7910 14644.99121
## 134    1721.1953 14812.71777
## 135    1719.6016 14671.64453
## 136    1718.0059 14364.54883
## 137    1716.4082 14450.76953
## 138    1714.8125 14229.99023
## 139    1713.2168 14338.12402
## 140    1711.6191 14388.28906
## 141    1710.0215 14283.48242
## 142    1708.4238 14192.80469
## 143    1706.8262 14180.38867
## 144    1705.2285 14435.44434
## 145    1703.6289 14232.19727
## 146    1702.0312 14294.82617
## 147    1700.4316 14324.60156
## 148    1698.8320 14493.45410
## 149    1697.2305 14484.11231
## 150    1695.6309 14715.34375
## 151    1694.0293 14929.29590
## 152    1692.4297 15055.71484
## 153    1690.8281 15294.48047
## 154    1689.2266 15534.70508
## 155    1687.6230 16018.25977
## 156    1686.0215 16537.49219
## 157    1684.4180 16487.17188
## 158    1682.8164 16837.65820
## 159    1681.2129 17571.54492
## 160    1679.6074 17630.15234
## 161    1678.0039 18042.50781
## 162    1676.4004 18428.19922
## 163    1674.7949 18863.57617
## 164    1673.1895 18932.75781
## 165    1671.5840 19414.55469
## 166    1669.9785 19488.25977
## 167    1668.3730 19700.45898
## 168    1666.7656 20139.76562
## 169    1665.1582 20133.91992
## 170    1663.5508 20252.51562
## 171    1661.9434 20056.91602
## 172    1660.3359 20195.68750
## 173    1658.7285 20477.41406
## 174    1657.1191 20277.22852
## 175    1655.5098 20448.49414
## 176    1653.9004 20262.37891
## 177    1652.2910 20172.63867
## 178    1650.6816 19971.13086
## 179    1649.0723 20098.84180
## 180    1647.4609 19897.43555
## 181    1645.8496 19770.60352
## 182    1644.2383 19518.15039
## 183    1642.6270 19351.11914
## 184    1641.0156 19103.51367
## 185    1639.4023 18866.86719
## 186    1637.7891 18500.10352
## 187    1636.1758 18384.57422
## 188    1634.5625 17942.13867
## 189    1632.9492 17267.51172
## 190    1631.3359 16780.58398
## 191    1629.7207 16561.76367
## 192    1628.1055 16025.64941
## 193    1626.4922 15656.93457
## 194    1624.8750 15396.70606
## 195    1623.2598 15268.10254
## 196    1621.6445 14788.40625
## 197    1620.0273 14553.27246
## 198    1618.4102 14293.49707
## 199    1616.7930 14146.68457
## 200    1615.1758 14213.22461
## 201    1613.5586 14154.56934
## 202    1611.9395 14077.40039
## 203    1610.3223 13805.64258
## 204    1608.7031 13853.66504
## 205    1607.0840 13968.05566
## 206    1605.4629 13995.95410
## 207    1603.8438 13792.34766
## 208    1602.2227 13821.81348
## 209    1600.6035 13889.83203
## 210    1598.9824 13805.13281
## 211    1597.3613 13466.04394
## 212    1595.7383 13450.84473
## 213    1594.1172 13355.50195
## 214    1592.4941 13388.10059
## 215    1590.8711 13377.54688
## 216    1589.2480 13474.82031
## 217    1587.6250 13337.97070
## 218    1586.0020 13230.43262
## 219    1584.3770 13276.87500
## 220    1582.7539 13306.36719
## 221    1581.1289 13234.29199
## 222    1579.5039 13073.02246
## 223    1577.8789 13251.72656
## 224    1576.2520 13192.00977
## 225    1574.6270 13166.13867
## 226    1573.0000 13301.66992
## 227    1571.3730 13143.62695
## 228    1569.7461 13113.17188
## 229    1568.1172 13007.46191
## 230    1566.4902 13040.01269
## 231    1564.8613 13063.33691
## 232    1563.2344 13198.71191
## 233    1561.6055 13168.27441
## 234    1559.9746 12992.07715
## 235    1558.3457 13085.95801
## 236    1556.7148 13279.49316
## 237    1555.0859 13160.12109
## 238    1553.4551 13163.44922
## 239    1551.8242 13015.02734
## 240    1550.1934 13254.38379
## 241    1548.5605 13024.80078
## 242    1546.9277 13032.74707
## 243    1545.2969 13101.94922
## 244    1543.6641 12909.29102
## 245    1542.0312 13056.56055
## 246    1540.3965 12862.46191
## 247    1538.7637 13073.94629
## 248    1537.1289 13100.22559
## 249    1535.4941 12913.88672
## 250    1533.8594 13057.93652
## 251    1532.2246 13036.80469
## 252    1530.5898 13179.23731
## 253    1528.9531 13170.31250
## 254    1527.3164 13338.62793
## 255    1525.6797 13306.75781
## 256    1524.0430 13323.77246
## 257    1522.4062 13586.61426
## 258    1520.7695 13409.68262
## 259    1519.1309 13629.65625
## 260    1517.4922 13690.83691
## 261    1515.8535 13820.64356
## 262    1514.2148 13817.69238
## 263    1512.5762 14014.51367
## 264    1510.9355 13944.43652
## 265    1509.2949 13863.71777
## 266    1507.6543 14133.55273
## 267    1506.0137 14072.63379
## 268    1504.3730 14034.59082
## 269    1502.7324 14040.73047
## 270    1501.0898 14092.54688
## 271    1499.4473 14253.95215
## 272    1497.8047 14109.35547
## 273    1496.1621 14142.86133
## 274    1494.5195 14190.04394
## 275    1492.8750 14158.11035
## 276    1491.2324 14118.58496
## 277    1489.5879 14050.18750
## 278    1487.9434 14018.30176
## 279    1486.2969 14150.56250
## 280    1484.6523 13890.75000
## 281    1483.0059 13910.56543
## 282    1481.3613 14024.53613
## 283    1479.7148 13975.98144
## 284    1478.0664 13895.56445
## 285    1476.4199 14048.92578
## 286    1474.7734 14004.94629
## 287    1473.1250 14341.79394
## 288    1471.4766 14670.91211
## 289    1469.8281 14722.36523
## 290    1468.1797 15180.13574
## 291    1466.5293 15417.90723
## 292    1464.8809 15972.32519
## 293    1463.2305 16387.10352
## 294    1461.5801 16751.71289
## 295    1459.9297 17094.95898
## 296    1458.2793 17185.17773
## 297    1456.6270 17266.27344
## 298    1454.9766 17109.69922
## 299    1453.3242 16804.89062
## 300    1451.6719 16653.01758
## 301    1450.0195 16434.65234
## 302    1448.3652 16233.01660
## 303    1446.7129 16261.28516
## 304    1445.0586 16094.53809
## 305    1443.4043 16224.08106
## 306    1441.7500 16534.88672
## 307    1440.0938 16777.57812
## 308    1438.4395 16971.83203
## 309    1436.7832 17265.66406
## 310    1435.1270 17363.10742
## 311    1433.4707 17759.36328
## 312    1431.8145 17898.90820
## 313    1430.1582 17710.99805
## 314    1428.5000 18019.43359
## 315    1426.8418 17848.16211
## 316    1425.1855 17931.77734
## 317    1423.5254 17756.07422
## 318    1421.8672 17523.17578
## 319    1420.2090 17572.14453
## 320    1418.5488 17563.84570
## 321    1416.8887 17505.84766
## 322    1415.2285 17484.01172
## 323    1413.5684 17525.41406
## 324    1411.9062 17283.82617
## 325    1410.2461 17668.35547
## 326    1408.5840 17625.43359
## 327    1406.9219 17671.27734
## 328    1405.2598 17634.38672
## 329    1403.5977 17647.12695
## 330    1401.9336 17698.94531
## 331    1400.2695 17884.49609
## 332    1398.6074 18048.93164
## 333    1396.9434 18428.11523
## 334    1395.2773 18921.28125
## 335    1393.6133 19402.21875
## 336    1391.9473 20316.81836
## 337    1390.2812 21210.00781
## 338    1388.6152 22039.78320
## 339    1386.9492 23043.23633
## 340    1385.2832 23708.75586
## 341    1383.6152 24699.41406
## 342    1381.9492 25080.91016
## 343    1380.2812 25520.70312
## 344    1378.6133 26060.71484
## 345    1376.9453 26281.33984
## 346    1375.2754 26290.64258
## 347    1373.6055 26335.88477
## 348    1371.9375 26210.40234
## 349    1370.2676 26134.37305
## 350    1368.5957 25729.09180
## 351    1366.9258 25205.76562
## 352    1365.2539 24597.38672
## 353    1363.5840 23742.46484
## 354    1361.9121 22874.45312
## 355    1360.2402 22000.83203
## 356    1358.5664 21202.32812
## 357    1356.8945 20519.24414
## 358    1355.2207 19915.64453
## 359    1353.5469 19811.24414
## 360    1351.8730 19662.08203
## 361    1350.1992 19601.08203
## 362    1348.5254 19443.07031
## 363    1346.8496 19577.66211
## 364    1345.1738 19185.44336
## 365    1343.4980 18763.55273
## 366    1341.8223 18499.95117
## 367    1340.1465 17938.21289
## 368    1338.4688 17633.15430
## 369    1336.7930 17516.04297
## 370    1335.1152 17425.80469
## 371    1333.4375 17311.76562
## 372    1331.7578 17449.52344
## 373    1330.0801 17280.41992
## 374    1328.4004 17453.85742
## 375    1326.7207 17323.52930
## 376    1325.0410 17441.80859
## 377    1323.3613 17521.34766
## 378    1321.6816 17242.31641
## 379    1320.0000 17254.92773
## 380    1318.3184 17532.22656
## 381    1316.6367 17320.27344
## 382    1314.9551 17237.72656
## 383    1313.2734 17060.10547
## 384    1311.5918 16996.96289
## 385    1309.9082 17110.61133
## 386    1308.2246 17268.76172
## 387    1306.5410 17950.96875
## 388    1304.8574 18566.07617
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## 922     348.8008  9473.81348
## 923     346.8965  9336.36328
## 924     344.9922  9238.36133
## 925     343.0879  9271.65723
## 926     341.1836  9324.62012
## 927     339.2773  9237.18457
## 928     337.3711  9385.87988
## 929     335.4648  9280.11133
## 930     333.5586  9363.17773
## 931     331.6504  9461.93262
## 932     329.7422  9568.50488
## 933     327.8340  9597.73242
## 934     325.9258  9645.28418
## 935     324.0176  9738.64453
## 936     322.1074  9588.45508
## 937     320.1973  9717.12500
## 938     318.2871  9742.35938
## 939     316.3750  9966.43945
## 940     314.4648  9909.20117
## 941     312.5527  9963.14258
## 942     310.6406  9975.22266
## 943     308.7285 10004.28906
## 944     306.8145  9900.05273
## 945     304.9004 10019.26856
## 946     302.9863 10052.22363
## 947     301.0723 10198.77344
## 948     299.1582 10132.45019
## 949     297.2422 10329.81934
## 950     295.3262 10404.43066
## 951     293.4102 10557.28809
## 952     291.4941 10488.33496
## 953     289.5762 10424.62891
## 954     287.6582 10659.51367
## 955     285.7402 10749.60644
## 956     283.8223 10781.01562
## 957     281.9043 10803.29004
## 958     279.9844 11071.73828
## 959     278.0645 11119.98144
## 960     276.1445 11351.77930
## 961     274.2246 11457.20996
## 962     272.3027 11385.60644
## 963     270.3809 11524.80957
## 964     268.4590 11762.80566
## 965     266.5371 11949.97559
## 966     264.6133 12109.75586
## 967     262.6914 12308.45898
## 968     260.7676 12607.13867
## 969     258.8438 12704.29394
## 970     256.9180 12965.07617
## 971     254.9941 13115.35254
## 972     253.0684 13610.92871
## 973     251.1426 14067.33594
## 974     249.2148 14337.95117
## 975     247.2891 14808.25684
## 976     245.3613 15319.85840
## 977     243.4336 15536.83008
## 978     241.5059 16159.54004
## 979     239.5781 16271.22168
## 980     237.6484 16630.39062
## 981     235.7188 17147.47266
## 982     233.7891 17300.30664
## 983     231.8594 17455.66211
## 984     229.9277 17515.13086
## 985     227.9961 17625.05859
## 986     226.0645 17281.95508
## 987     224.1328 17393.18164
## 988     222.2012 17543.16797
## 989     220.2676 17749.99219
## 990     218.3340 17736.90430
## 991     216.4004 17987.54883
## 992     214.4668 18147.60547
## 993     212.5312 18298.54492
## 994     210.5957 18541.14062
## 995     208.6602 18777.16797
## 996     206.7246 18692.84766
## 997     204.7871 18755.74023
## 998     202.8516 18587.56836
## 999     200.9141 18403.98828
## 1000    198.9766 18238.55273
## 1001    197.0371 18026.75977
## 1002    195.0996 17735.11133
## 1003    193.1602 17448.75391
## 1004    191.2207 16848.01953
## 1005    189.2812 16975.65430
## 1006    187.3398 16538.92383
## 1007    185.3984 16421.83789
## 1008    183.4570 16307.37988
## 1009    181.5156 16137.61231
## 1010    179.5742 16325.76758
## 1011    177.6309 16295.06152
## 1012    175.6875 16369.87500
## 1013    173.7441 16601.58984
## 1014    171.8008 17035.11133
## 1015    169.8574 17168.85352
## 1016    167.9121 17688.18164
## 1017    165.9668 18193.14062
## 1018    164.0215 18556.53320
## 1019    162.0742 19063.64062
## 1020    160.1289 19886.46484
## 1021    158.1816 20444.41016
## 1022    156.2344 21388.51367
## 1023    154.2852 21834.21289
## 1024    152.3379   546.63123
## 1025    150.3887    30.78923
## 1026    148.4395    33.34753
## 1027    146.4902    17.95235
## 1028    144.5391    39.74273
## 1029    142.5898    21.78953
## 1030    140.6387    25.62901
## 1031    138.6875    21.77980

2 Test plot section 2

2.1 with subsection

Plot spectral analysis

library(ggpubr)

test2plot <- ggplot(data = test2_10_8)+
  geom_path(aes(x = Raman_shift, y = Counts))

testcontrolplot <- ggplot(data = test_control)+
  geom_path(aes(x = Raman_shift, y = Counts))

potplastplot <- ggplot(data = pot_plastic)+
  geom_path(aes(x = Raman_shift, y = Counts))

pot1plastplot <- ggplot(data = pot_plastic1)+
  geom_path(aes(x = Raman_shift, y = Counts))

testcontrolplot

test2plot

ggarrange(test2plot, testcontrolplot, potplastplot, pot1plastplot)

setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/9_18_19_spectral_data/")

#list.files()
#plot for white filter control w/oil
oil_control <- read.delim("renB531_control_w_oil_0__Time_0.txt", header = F, sep = "\t")
colnames(oil_control) <- c("Raman_shift", "Counts")
test_control <- as.data.frame(oil_control)

oilcontrolplot <- ggplot(data = oil_control)+
  geom_path(aes(x = Raman_shift, y = Counts))

oilcontrolplot

#plot of individual microspheres on white filter w/oil
micro_indv <- read.delim("renD6C5_microsphere_ind_0__Time_0.txt", header = F, sep = "\t")
colnames(micro_indv) <- c("Raman_shift", "Counts")
micro_indv <- as.data.frame(micro_indv)
micro_indv_plot <-  ggplot(micro_indv, aes(x = Raman_shift, y = Counts))+
  geom_path()
micro_indv_plot

micro_indv1 <- read.delim("renC2B9_microsphere_ind1_0__Time_0.txt", header = F, sep = "\t")
colnames(micro_indv1) <- c("Raman_shift", "Counts")
micro_indv1 <- as.data.frame(micro_indv1)
micro_indv1_plot <-  ggplot(micro_indv1, aes(x = Raman_shift, y = Counts))+
  geom_path()
micro_indv1_plot

micro_indv2 <- read.delim("ren3CA9_microsphere_ind2_0__Time_0.txt", header = F, sep = "\t")
colnames(micro_indv2) <- c("Raman_shift", "Counts")
micro_indv2 <- as.data.frame(micro_indv2)

micro_indv2_plot <-  ggplot(micro_indv2, aes(x = Raman_shift, y = Counts))+
  geom_path()
micro_indv2_plot

#Now reading and plotting with single function using sourced function
micro_indv3_plot <- Raman_plot_function("ren7832_microsphere_ind3_0__Time_0.txt")
micro_indv3_plot

micro_indv4_plot <- Raman_plot_function("ren3359_microsphere_spec_0__Time_0.txt")
micro_indv4_plot

#Black filter
pot_plastic_st4 <-  Raman_plot_function("renE0D5a_st4_potplastic_0__Time_0.txt")
pot_plastic_st4

#White filter clusters of microspheres


#Plot of all indv microsphere plots
ggarrange(oilcontrolplot, micro_indv_plot, micro_indv2_plot, micro_indv3_plot)

Boilcontrolplot <- Raman_plot_function("renED08Z_blackfilter_control_wOIL_0__Time_0.txt")
Boilcontrolplot

#Black filter plot
ggarrange(Boilcontrolplot, pot_plastic_st4)

setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/9_18_19_spectral_data/")

micro_indv_pic <- readPNG("microsphere_w_scalebar_ind3.PNG")
micro_indv_pic <- as.raster(micro_indv_pic)
plot(micro_indv_pic)

micro_indv1_pic <- read.bmp("microsphere1.bmp")
micro_indv1_pic <- pixmapRGB(micro_indv1_pic)
plot(micro_indv1_pic)

3 Another Subsection

#baselineGUI() need to install gWidgets to use this functionality

str(milk)

## 'data.frame':    45 obs. of  2 variables:
##  $ cow    : num  0 0.25 0.375 0.875 0.5 0.75 0.5 0.125 0 0.125 ...
##  $ spectra: num [1:45, 1:21451] 1029 371 606 368 554 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : NULL
##   .. ..$ : chr [1:21451] "4999.94078628963" "5001.55954267662" "5003.17856106153" "5004.79784144435" ...
##  - attr(*, "terms")=Classes 'terms', 'formula'  language cow ~ spectra
##   .. ..- attr(*, "variables")= language list(cow, spectra)
##   .. ..- attr(*, "factors")= int [1:2, 1] 0 1
##   .. .. ..- attr(*, "dimnames")=List of 2
##   .. .. .. ..$ : chr [1:2] "cow" "spectra"
##   .. .. .. ..$ : chr "spectra"
##   .. ..- attr(*, "term.labels")= chr "spectra"
##   .. ..- attr(*, "order")= int 1
##   .. ..- attr(*, "intercept")= int 1
##   .. ..- attr(*, "response")= int 1
##   .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
##   .. ..- attr(*, "predvars")= language list(cow, spectra)
##   .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "nmatrix.21451"
##   .. .. ..- attr(*, "names")= chr [1:2] "cow" "spectra"

#View(milk$spectra[1,,drop = F])

test_control_polymod <-  read.delim("renB232_0__Time_0_test_control.txt", header = F, sep = "\t")

#head(test_control_polymod)

test_control_polymod1 <- t(test_control_polymod)

#View(test_control_polymod1)

colnames(test_control_polymod1) <- test_control_polymod1[1,]
test_control_polymod2 <- test_control_polymod1[-1,]

#View(test_control_polymod2)

test_control_polymod3 <- t(test_control_polymod2)

#View(test_control_polymod3)

test_control_polymod4 <- baseline.modpolyfit(test_control_polymod3, degree = 2)

#View(test_control_polymod4)
#View(test_control_polymod4$baseline)

#View(test_control_polymod4$corrected)

#the plotting works using the S4 type data brom the baseline function
#however, that isn't easy to work with and hard to extract the corrected plot
#I need to figure a way to plot the data to and extract the plot so as to
#compare to the library data.

data(milk)
#The way the functions are used have changed and some arguments are no longer used.
#ANY object using baseline() will be broken and I dont need to fix
#bc.modpolyfit <- baseline.modpolyfit(milk$spectra[1,, drop=FALSE], method='modpolyfit', deg=3)

#plot(bc.modpolyfit)

##View(bc.modpolyfit@baseline)

#test_control_polymod5 <- baseline(test_control_polymod1, method = 'modpolyfit', deg = 3)

#plot(test_control_polymod5)

#test_control_polymod6 <- baseline(test_control_polymod3, method = 'modpolyfit', deg = 6)

#plot(test_control_polymod6)

#plot(test_control_polymod6@spectra)

#View(test_control_polymod6@spectra)

#View(test_control_polymod6@corrected)

#View(test_control_polymod6@baseline)

setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/9_18_19_spectral_data/")

#test_micro_indv_polymod_df <- Raman_df_function("ren3CA9_microsphere_ind2_0__Time_0.txt")

#test_micro_indv_polymod_df_transpose <- t(test_micro_indv_polymod_df)

#colnames(test_micro_indv_polymod_df) <- test_micro_indv_polymod_df[1,]
#test

#class(test_control_polymod6)

#showMethods(class = "baseline")
#method ? plot("baseline")


#test_micro_indv_polymod_df
#baseline can't be converted into data frame so can't use ggplot2

#Three separate packages have baseline corrections using basically the same algorithm
#prospectr::baselines(), Baseline::baseline.modpolyfit(), hyperSpec::spc.fit.poly()
#prospectr is the only one that does it baseline correction differently and not the common way so I will not use that. The hyperSpec is made to be used with hyperspec obj so I will continue with that one. Currently the 

setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/10_17_19_spectral_data/")

Bfilter_sample4_plot <- Raman_plot_function("ren68C2_Bfilter_sample4_fullrange_0__Time_0.txt")

Bfilter_sample4_plot

Bfilter_sample5_plot <- Raman_plot_function("ren41A6_Bfilter_sample5_fullrange_0__Time_0.txt")

Bfilter_sample5_plot

Bfilter_control_plot <- Raman_plot_function("renF6B0_Bfilter_rerun_control_0__Time_0.txt")

Bfilter_control_plot

Baseline package is a bust and difficult to work with with no method for resolving baseline objects…seems like hyperspec has more flexibilty

setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/10_24_19_spectral_data/")

potP14plot <- Raman_plot_function("renEFCC_Bfilter_potP14_0__Time_0.txt")

potP14plot

#baselines <- spc.fit.poly.below(test_control_polymod3)
#need to convert to hyperspec object first

plot(chondro)

#convert to hyperspec object
hyper_test <- as.hyperSpec(test_control_polymod3)

plot(hyper_test)

#Baselines
baselines <-  spc.fit.poly.below(hyper_test, poly.order = 6)
#Plotting baseline corrections
plot(hyper_test-baselines)

#Notes on hyperspec objects and their structure.

S4 objects with an S3 data frame object within it. Assumes that a data frame is providing “meta data” *matrix is assumed to be the spectral data

For my larger data sets I need to form a list with extra data (environmental) and internal matrix with all of the spectra for a given sample. Ideally I would have all spectra for a sample within 1 matrix for identification purposes.

I have created a basic hyperspec with 1 spectra above now… Can I apply baseline corrections into hyper_test data? From there I can do the standard normal variate correction and min-max standardization. Then I could finally take this data to compare to the library.

hyper_baseline_corr <- hyper_test-baselines

plot(hyper_baseline_corr)

#Now that I have baseline corrected data I can normalize and min max standardize

hyper_baseline_corr@data[["spc"]] <- standardNormalVariate(hyper_baseline_corr@data[["spc"]])

hyper_baseline_corr@data[["spc"]] <- (hyper_baseline_corr@data[["spc"]] - min(hyper_baseline_corr@data[["spc"]])) / (max(hyper_baseline_corr@data[["spc"]])-min(hyper_baseline_corr@data[["spc"]]))

plot(hyper_baseline_corr)

In the previous chunk, I was able to baseline correct, standard variate normalize and min-max scale the spectra to be able to compare to a library. Next step is to import the reference library to compare spectra. Then I can do correlations to determine the likely polymer type.

I also need to alter the the original data set to be in the same matter as the reference library. The library is a 1 wavelength step the current data I have approx to a 2 wavelength step between values. Need to use the approxfun() to interpolate on the frequency index they used to have the exact same number of values for the eventual correlation matrix. The frequency goes from 780 min to 1750 max by steps of 1. Need to subset my initial data according to this.

ref_lib <- read.csv("/Users/christophercarnivale/Desktop/Dissertation_data/spectra_db.csv")

str(ref_lib)

## 'data.frame':    451515 obs. of  8 variables:
##  $ X        : int  1262413 1262513 1262613 1262713 1262813 1262913 1263013 1263113 1263213 1263313 ...
##  $ ID       : chr  "ABS 1" "ABS 1" "ABS 1" "ABS 1" ...
##  $ SAMPLE   : chr  "ABS" "ABS" "ABS" "ABS" ...
##  $ INTENSITY: num  0.119 0.114 0.11 0.113 0.109 ...
##  $ WAVE     : int  780 781 782 783 784 785 786 787 788 789 ...
##  $ PARTICLE : chr  "1" "1" "1" "1" ...
##  $ SOURCE   : chr  "plastic" "plastic" "plastic" "plastic" ...
##  $ poly_lab : chr  "ABS" "ABS" "ABS" "ABS" ...

plastic_ref_lib <- ref_lib %>% 
  dplyr::filter(SOURCE == "plastic")

#create aprrox wavelength from particle data to get the same number of data
#points for a correlation analysis

freq_index <- seq(780, 1750, by = 1)

plastic_approx_fun <- approxfun(x = hyper_baseline_corr@wavelength, hyper_baseline_corr@data[["spc"]])
#test approx
plastic_approx <- plastic_approx_fun(freq_index)

cbind(freq_index, plastic_approx)

##        freq_index plastic_approx
##   [1,]        780   0.0072165391
##   [2,]        781   0.0065858939
##   [3,]        782   0.0065644072
##   [4,]        783   0.0068442473
##   [5,]        784   0.0072408617
##   [6,]        785   0.0070251642
##   [7,]        786   0.0068388549
##   [8,]        787   0.0072754733
##   [9,]        788   0.0075854061
##  [10,]        789   0.0076216350
##  [11,]        790   0.0077366558
##  [12,]        791   0.0079256932
##  [13,]        792   0.0085413678
##  [14,]        793   0.0093272308
##  [15,]        794   0.0107410908
##  [16,]        795   0.0120459996
##  [17,]        796   0.0120963881
##  [18,]        797   0.0126662281
##  [19,]        798   0.0143583412
##  [20,]        799   0.0150228073
##  [21,]        800   0.0147368923
##  [22,]        801   0.0166086599
##  [23,]        802   0.0193166032
##  [24,]        803   0.0189128285
##  [25,]        804   0.0184728911
##  [26,]        805   0.0196257495
##  [27,]        806   0.0210115732
##  [28,]        807   0.0228744213
##  [29,]        808   0.0256721762
##  [30,]        809   0.0292948487
##  [31,]        810   0.0333401026
##  [32,]        811   0.0375366789
##  [33,]        812   0.0388921344
##  [34,]        813   0.0405214952
##  [35,]        814   0.0448558852
##  [36,]        815   0.0496888982
##  [37,]        816   0.0554495188
##  [38,]        817   0.0609914138
##  [39,]        818   0.0663577118
##  [40,]        819   0.0713293429
##  [41,]        820   0.0761775143
##  [42,]        821   0.0849160360
##  [43,]        822   0.0936216816
##  [44,]        823   0.1015360616
##  [45,]        824   0.1091735488
##  [46,]        825   0.1163609069
##  [47,]        826   0.1247434476
##  [48,]        827   0.1339570746
##  [49,]        828   0.1451415831
##  [50,]        829   0.1568404234
##  [51,]        830   0.1705669387
##  [52,]        831   0.1837704138
##  [53,]        832   0.1950624172
##  [54,]        833   0.2080540267
##  [55,]        834   0.2233561618
##  [56,]        835   0.2401616882
##  [57,]        836   0.2578395547
##  [58,]        837   0.2731775184
##  [59,]        838   0.2884258066
##  [60,]        839   0.3096142115
##  [61,]        840   0.3291962795
##  [62,]        841   0.3443372985
##  [63,]        842   0.3638000511
##  [64,]        843   0.3880096355
##  [65,]        844   0.4065765197
##  [66,]        845   0.4225785401
##  [67,]        846   0.4372506776
##  [68,]        847   0.4520741462
##  [69,]        848   0.4692337962
##  [70,]        849   0.4837267607
##  [71,]        850   0.4927107073
##  [72,]        851   0.4968237900
##  [73,]        852   0.4967719227
##  [74,]        853   0.4982258269
##  [75,]        854   0.5001764406
##  [76,]        855   0.4975764567
##  [77,]        856   0.4943328416
##  [78,]        857   0.4888935429
##  [79,]        858   0.4827113348
##  [80,]        859   0.4753982924
##  [81,]        860   0.4662572300
##  [82,]        861   0.4559539433
##  [83,]        862   0.4446179268
##  [84,]        863   0.4330297593
##  [85,]        864   0.4206377020
##  [86,]        865   0.4081079506
##  [87,]        866   0.3951897788
##  [88,]        867   0.3848613242
##  [89,]        868   0.3773551287
##  [90,]        869   0.3651867898
##  [91,]        870   0.3509754843
##  [92,]        871   0.3387120805
##  [93,]        872   0.3270258951
##  [94,]        873   0.3179571226
##  [95,]        874   0.3070617927
##  [96,]        875   0.2927684484
##  [97,]        876   0.2819255675
##  [98,]        877   0.2736823561
##  [99,]        878   0.2622456065
## [100,]        879   0.2499893815
## [101,]        880   0.2412054837
## [102,]        881   0.2317411677
## [103,]        882   0.2200049503
## [104,]        883   0.2092070991
## [105,]        884   0.1995507233
## [106,]        885   0.1904241037
## [107,]        886   0.1815562022
## [108,]        887   0.1772974269
## [109,]        888   0.1733600782
## [110,]        889   0.1676315847
## [111,]        890   0.1631764030
## [112,]        891   0.1612163193
## [113,]        892   0.1589367497
## [114,]        893   0.1564067121
## [115,]        894   0.1559942127
## [116,]        895   0.1561472320
## [117,]        896   0.1536796561
## [118,]        897   0.1525293073
## [119,]        898   0.1557291254
## [120,]        899   0.1568372267
## [121,]        900   0.1554602944
## [122,]        901   0.1559767148
## [123,]        902   0.1573774216
## [124,]        903   0.1566299781
## [125,]        904   0.1557147472
## [126,]        905   0.1551549473
## [127,]        906   0.1540074548
## [128,]        907   0.1518034008
## [129,]        908   0.1492501929
## [130,]        909   0.1464482561
## [131,]        910   0.1461697740
## [132,]        911   0.1464075550
## [133,]        912   0.1450199292
## [134,]        913   0.1443595946
## [135,]        914   0.1456900219
## [136,]        915   0.1444672248
## [137,]        916   0.1406306566
## [138,]        917   0.1379149864
## [139,]        918   0.1356294991
## [140,]        919   0.1345441604
## [141,]        920   0.1336839951
## [142,]        921   0.1335723950
## [143,]        922   0.1307122744
## [144,]        923   0.1239312723
## [145,]        924   0.1184736475
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## [621,]       1400   0.1589332578
## [622,]       1401   0.1564915704
## [623,]       1402   0.1535747565
## [624,]       1403   0.1508507609
## [625,]       1404   0.1491909302
## [626,]       1405   0.1491118477
## [627,]       1406   0.1488214776
## [628,]       1407   0.1481890537
## [629,]       1408   0.1443953159
## [630,]       1409   0.1424280189
## [631,]       1410   0.1430245951
## [632,]       1411   0.1421703961
## [633,]       1412   0.1408573669
## [634,]       1413   0.1396868208
## [635,]       1414   0.1388976589
## [636,]       1415   0.1386106808
## [637,]       1416   0.1391474280
## [638,]       1417   0.1405110326
## [639,]       1418   0.1465273577
## [640,]       1419   0.1489837340
## [641,]       1420   0.1471096119
## [642,]       1421   0.1479983687
## [643,]       1422   0.1495409496
## [644,]       1423   0.1505865094
## [645,]       1424   0.1518668552
## [646,]       1425   0.1534071086
## [647,]       1426   0.1558449593
## [648,]       1427   0.1580904801
## [649,]       1428   0.1582245320
## [650,]       1429   0.1588156760
## [651,]       1430   0.1598639121
## [652,]       1431   0.1588003878
## [653,]       1432   0.1574175347
## [654,]       1433   0.1563750621
## [655,]       1434   0.1529090946
## [656,]       1435   0.1472879167
## [657,]       1436   0.1423957487
## [658,]       1437   0.1379552897
## [659,]       1438   0.1347637139
## [660,]       1439   0.1301735494
## [661,]       1440   0.1244869309
## [662,]       1441   0.1185669125
## [663,]       1442   0.1134528821
## [664,]       1443   0.1108292496
## [665,]       1444   0.1084861752
## [666,]       1445   0.1063335123
## [667,]       1446   0.1057110706
## [668,]       1447   0.1064582374
## [669,]       1448   0.1103696525
## [670,]       1449   0.1118303451
## [671,]       1450   0.1118809319
## [672,]       1451   0.1132456610
## [673,]       1452   0.1152404986
## [674,]       1453   0.1184719564
## [675,]       1454   0.1239252152
## [676,]       1455   0.1303181729
## [677,]       1456   0.1314504733
## [678,]       1457   0.1315717381
## [679,]       1458   0.1299938286
## [680,]       1459   0.1289645037
## [681,]       1460   0.1279680558
## [682,]       1461   0.1245953183
## [683,]       1462   0.1184223633
## [684,]       1463   0.1083812026
## [685,]       1464   0.1004662246
## [686,]       1465   0.0933155268
## [687,]       1466   0.0871075177
## [688,]       1467   0.0800079943
## [689,]       1468   0.0719059792
## [690,]       1469   0.0693380632
## [691,]       1470   0.0669654344
## [692,]       1471   0.0596929639
## [693,]       1472   0.0540365195
## [694,]       1473   0.0498513853
## [695,]       1474   0.0468435153
## [696,]       1475   0.0440626688
## [697,]       1476   0.0414827002
## [698,]       1477   0.0402681996
## [699,]       1478   0.0400421694
## [700,]       1479   0.0392729721
## [701,]       1480   0.0388874590
## [702,]       1481   0.0395606385
## [703,]       1482   0.0388796513
## [704,]       1483   0.0374325457
## [705,]       1484   0.0386193781
## [706,]       1485   0.0406983069
## [707,]       1486   0.0444220433
## [708,]       1487   0.0424049178
## [709,]       1488   0.0383106389
## [710,]       1489   0.0399917707
## [711,]       1490   0.0416074057
## [712,]       1491   0.0431296067
## [713,]       1492   0.0439189238
## [714,]       1493   0.0444032356
## [715,]       1494   0.0443059273
## [716,]       1495   0.0448726590
## [717,]       1496   0.0461574169
## [718,]       1497   0.0454143807
## [719,]       1498   0.0442268761
## [720,]       1499   0.0428245307
## [721,]       1500   0.0424951588
## [722,]       1501   0.0430340249
## [723,]       1502   0.0395760449
## [724,]       1503   0.0372976673
## [725,]       1504   0.0393280755
## [726,]       1505   0.0398044694
## [727,]       1506   0.0393562001
## [728,]       1507   0.0396172300
## [729,]       1508   0.0396390191
## [730,]       1509   0.0391893991
## [731,]       1510   0.0407139011
## [732,]       1511   0.0426480503
## [733,]       1512   0.0385425186
## [734,]       1513   0.0353963676
## [735,]       1514   0.0335544444
## [736,]       1515   0.0323618162
## [737,]       1516   0.0315463726
## [738,]       1517   0.0318934501
## [739,]       1518   0.0308012772
## [740,]       1519   0.0283141359
## [741,]       1520   0.0260811472
## [742,]       1521   0.0243297722
## [743,]       1522   0.0240587278
## [744,]       1523   0.0219107922
## [745,]       1524   0.0184786679
## [746,]       1525   0.0175825023
## [747,]       1526   0.0169371874
## [748,]       1527   0.0165825603
## [749,]       1528   0.0178944346
## [750,]       1529   0.0198407099
## [751,]       1530   0.0190023342
## [752,]       1531   0.0168859766
## [753,]       1532   0.0129317575
## [754,]       1533   0.0116403732
## [755,]       1534   0.0113732119
## [756,]       1535   0.0126513949
## [757,]       1536   0.0117826606
## [758,]       1537   0.0088167414
## [759,]       1538   0.0104359440
## [760,]       1539   0.0118060780
## [761,]       1540   0.0101788145
## [762,]       1541   0.0085842363
## [763,]       1542   0.0070111309
## [764,]       1543   0.0084854392
## [765,]       1544   0.0093164146
## [766,]       1545   0.0086813610
## [767,]       1546   0.0087463642
## [768,]       1547   0.0091691425
## [769,]       1548   0.0103903728
## [770,]       1549   0.0110905831
## [771,]       1550   0.0111262034
## [772,]       1551   0.0105083903
## [773,]       1552   0.0102933901
## [774,]       1553   0.0127015769
## [775,]       1554   0.0149774713
## [776,]       1555   0.0171428849
## [777,]       1556   0.0186684541
## [778,]       1557   0.0186858710
## [779,]       1558   0.0150733767
## [780,]       1559   0.0119871565
## [781,]       1560   0.0092681853
## [782,]       1561   0.0099726072
## [783,]       1562   0.0109439084
## [784,]       1563   0.0123247771
## [785,]       1564   0.0122757318
## [786,]       1565   0.0115712203
## [787,]       1566   0.0095142858
## [788,]       1567   0.0097482992
## [789,]       1568   0.0121854814
## [790,]       1569   0.0126125161
## [791,]       1570   0.0125729309
## [792,]       1571   0.0119462850
## [793,]       1572   0.0106388281
## [794,]       1573   0.0089262779
## [795,]       1574   0.0135313249
## [796,]       1575   0.0160617654
## [797,]       1576   0.0151055651
## [798,]       1577   0.0128501051
## [799,]       1578   0.0106301926
## [800,]       1579   0.0118444748
## [801,]       1580   0.0133146721
## [802,]       1581   0.0150448143
## [803,]       1582   0.0140989330
## [804,]       1583   0.0142903660
## [805,]       1584   0.0191790965
## [806,]       1585   0.0214554408
## [807,]       1586   0.0221512513
## [808,]       1587   0.0204009904
## [809,]       1588   0.0188495510
## [810,]       1589   0.0176374584
## [811,]       1590   0.0180631323
## [812,]       1591   0.0190219431
## [813,]       1592   0.0199326825
## [814,]       1593   0.0208982230
## [815,]       1594   0.0219172951
## [816,]       1595   0.0232260826
## [817,]       1596   0.0244157433
## [818,]       1597   0.0251608742
## [819,]       1598   0.0257840553
## [820,]       1599   0.0263723150
## [821,]       1600   0.0288648052
## [822,]       1601   0.0298402890
## [823,]       1602   0.0285066316
## [824,]       1603   0.0289995125
## [825,]       1604   0.0299814685
## [826,]       1605   0.0307792656
## [827,]       1606   0.0316347920
## [828,]       1607   0.0325400704
## [829,]       1608   0.0301859372
## [830,]       1609   0.0283207676
## [831,]       1610   0.0283214388
## [832,]       1611   0.0295107192
## [833,]       1612   0.0312998600
## [834,]       1613   0.0336269540
## [835,]       1614   0.0376140991
## [836,]       1615   0.0437020191
## [837,]       1616   0.0423284911
## [838,]       1617   0.0409369401
## [839,]       1618   0.0455713514
## [840,]       1619   0.0492186361
## [841,]       1620   0.0521795091
## [842,]       1621   0.0562887600
## [843,]       1622   0.0607059647
## [844,]       1623   0.0656230096
## [845,]       1624   0.0701540077
## [846,]       1625   0.0748614385
## [847,]       1626   0.0817522088
## [848,]       1627   0.0872677090
## [849,]       1628   0.0914502523
## [850,]       1629   0.0972779357
## [851,]       1630   0.1034229717
## [852,]       1631   0.1098863891
## [853,]       1632   0.1166672893
## [854,]       1633   0.1238524767
## [855,]       1634   0.1355925874
## [856,]       1635   0.1449096161
## [857,]       1636   0.1511112536
## [858,]       1637   0.1549272993
## [859,]       1638   0.1589852489
## [860,]       1639   0.1658513001
## [861,]       1640   0.1734439787
## [862,]       1641   0.1815258254
## [863,]       1642   0.1854854617
## [864,]       1643   0.1879674190
## [865,]       1644   0.1880759152
## [866,]       1645   0.1907102602
## [867,]       1646   0.1940266784
## [868,]       1647   0.1967326129
## [869,]       1648   0.1989168181
## [870,]       1649   0.2006549060
## [871,]       1650   0.2034223439
## [872,]       1651   0.2056262541
## [873,]       1652   0.2064519141
## [874,]       1653   0.2077877247
## [875,]       1654   0.2088967642
## [876,]       1655   0.2060631327
## [877,]       1656   0.2070057954
## [878,]       1657   0.2118752079
## [879,]       1658   0.2104939260
## [880,]       1659   0.2090573401
## [881,]       1660   0.2097410530
## [882,]       1661   0.2058879440
## [883,]       1662   0.2001280968
## [884,]       1663   0.2008364895
## [885,]       1664   0.2007708023
## [886,]       1665   0.1997560265
## [887,]       1666   0.1978511284
## [888,]       1667   0.1957459144
## [889,]       1668   0.1935327991
## [890,]       1669   0.1895544345
## [891,]       1670   0.1845627300
## [892,]       1671   0.1812567587
## [893,]       1672   0.1760848106
## [894,]       1673   0.1682934906
## [895,]       1674   0.1661871888
## [896,]       1675   0.1641983171
## [897,]       1676   0.1575139943
## [898,]       1677   0.1493171636
## [899,]       1678   0.1401103504
## [900,]       1679   0.1375688174
## [901,]       1680   0.1327667591
## [902,]       1681   0.1244266297
## [903,]       1682   0.1172614506
## [904,]       1683   0.1106633136
## [905,]       1684   0.1051735456
## [906,]       1685   0.0976214477
## [907,]       1686   0.0885883463
## [908,]       1687   0.0832067510
## [909,]       1688   0.0781697632
## [910,]       1689   0.0735698486
## [911,]       1690   0.0669943463
## [912,]       1691   0.0605993712
## [913,]       1692   0.0578625350
## [914,]       1693   0.0544674195
## [915,]       1694   0.0505763391
## [916,]       1695   0.0469317908
## [917,]       1696   0.0446375686
## [918,]       1697   0.0446383269
## [919,]       1698   0.0419442243
## [920,]       1699   0.0384607861
## [921,]       1700   0.0350653012
## [922,]       1701   0.0326251763
## [923,]       1702   0.0309106009
## [924,]       1703   0.0326762206
## [925,]       1704   0.0343761321
## [926,]       1705   0.0357744272
## [927,]       1706   0.0330542199
## [928,]       1707   0.0301838330
## [929,]       1708   0.0323976907
## [930,]       1709   0.0327971728
## [931,]       1710   0.0318620132
## [932,]       1711   0.0326319127
## [933,]       1712   0.0331176894
## [934,]       1713   0.0330807267
## [935,]       1714   0.0341905464
## [936,]       1715   0.0349732598
## [937,]       1716   0.0329629447
## [938,]       1717   0.0358504193
## [939,]       1718   0.0421162355
## [940,]       1719   0.0409096971
## [941,]       1720   0.0408165882
## [942,]       1721   0.0424710327
## [943,]       1722   0.0436245132
## [944,]       1723   0.0446979778
## [945,]       1724   0.0459288165
## [946,]       1725   0.0467420074
## [947,]       1726   0.0474010626
## [948,]       1727   0.0534346129
## [949,]       1728   0.0577123378
## [950,]       1729   0.0596221123
## [951,]       1730   0.0602862705
## [952,]       1731   0.0616155925
## [953,]       1732   0.0658438459
## [954,]       1733   0.0685572697
## [955,]       1734   0.0705430187
## [956,]       1735   0.0742690683
## [957,]       1736   0.0757143609
## [958,]       1737   0.0745132094
## [959,]       1738   0.0788637984
## [960,]       1739   0.0824457001
## [961,]       1740   0.0819241089
## [962,]       1741   0.0818310009
## [963,]       1742   0.0819741210
## [964,]       1743   0.0825003616
## [965,]       1744   0.0802122520
## [966,]       1745   0.0751749856
## [967,]       1746   0.0747653156
## [968,]       1747   0.0732698489
## [969,]       1748   0.0686547718
## [970,]       1749   0.0724111392
## [971,]       1750   0.0772495915

#test savitzky-golay smoother - works like a charm nice and easy
plastic_approx_smoother <- sgolayfilt(plastic_approx, 3,11)

plot(hyper_baseline_corr@wavelength, hyper_baseline_corr@data[["spc"]], type = "l")+
lines(freq_index, plastic_approx, col = "red")+lines(freq_index, plastic_approx_smoother, col = "green")

## integer(0)

#Black means its approx poorly  - red is good approximation

unique(ref_lib$poly_lab)

##  [1] "ABS"  "HDPE" "LDPE" "PA"   "PC"   "PET"  "PLA"  "PMMA" "Poly" "POM" 
## [11] "PP"   "PS"   "PSA"  "PVC"  "."

abs_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "ABS")

cor.test(plastic_approx, abs_reference$INTENSITY, method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  plastic_approx and abs_reference$INTENSITY
## t = -3.2904, df = 969, p-value = 0.001036
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.16692691 -0.04248696
## sample estimates:
##        cor 
## -0.1051184

HDPE_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "HDPE")

cor.test(plastic_approx, HDPE_reference$INTENSITY)

## 
##  Pearson's product-moment correlation
## 
## data:  plastic_approx and HDPE_reference$INTENSITY
## t = 2.6683, df = 969, p-value = 0.007751
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.0226140 0.1475247
## sample estimates:
##        cor 
## 0.08540492

LDPE_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "LDPE")

cor.test(plastic_approx, LDPE_reference$INTENSITY)

## 
##  Pearson's product-moment correlation
## 
## data:  plastic_approx and LDPE_reference$INTENSITY
## t = 1.5896, df = 969, p-value = 0.1122
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.01195106  0.11354782
## sample estimates:
##        cor 
## 0.05099971

plot(freq_index, LDPE_reference$INTENSITY, type = "l")+
lines(freq_index, plastic_approx, col = "red")

## integer(0)

PA_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PA")

cor.test(plastic_approx, PA_reference$INTENSITY, method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  plastic_approx and PA_reference$INTENSITY
## t = -2.0275, df = 969, p-value = 0.04289
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.127384974 -0.002089523
## sample estimates:
##         cor 
## -0.06499341

plot(freq_index, PA_reference$INTENSITY, type = "l")+
lines(freq_index, plastic_approx, col = "red")

## integer(0)

PC_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PC")

cor.test(plastic_approx, PC_reference$INTENSITY, method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  plastic_approx and PC_reference$INTENSITY
## t = -4.4571, df = 969, p-value = 9.277e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.20284087 -0.07953396
## sample estimates:
##        cor 
## -0.1417372

plot(freq_index, PC_reference$INTENSITY, type = "l")+
lines(freq_index, plastic_approx, col = "red")

## integer(0)

PET_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PET")

cor.test(plastic_approx, PET_reference$INTENSITY, method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  plastic_approx and PET_reference$INTENSITY
## t = -3.6079, df = 969, p-value = 0.0003244
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.17676533 -0.05260185
## sample estimates:
##        cor 
## -0.1151333

PLA_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PLA")

cor.test(plastic_approx, PLA_reference$INTENSITY, method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  plastic_approx and PLA_reference$INTENSITY
## t = 1.8277, df = 969, p-value = 0.0679
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.004315159  0.121079132
## sample estimates:
##        cor 
## 0.05861318

PMMA_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PMMA")

cor.test(plastic_approx, PMMA_reference$INTENSITY, method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  plastic_approx and PMMA_reference$INTENSITY
## t = -3.2815, df = 969, p-value = 0.001069
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.16664984 -0.04220247
## sample estimates:
##        cor 
## -0.1048366

Poly_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "Poly")

cor.test(plastic_approx, Poly_reference$INTENSITY, method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  plastic_approx and Poly_reference$INTENSITY
## t = -5.7582, df = 969, p-value = 1.141e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2420367 -0.1203589
## sample estimates:
##       cor 
## -0.181894

POM_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "POM")

cor.test(plastic_approx, POM_reference$INTENSITY, method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  plastic_approx and POM_reference$INTENSITY
## t = 4.6299, df = 969, p-value = 4.157e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.08498897 0.20810118
## sample estimates:
##       cor 
## 0.1471148

PP_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PP")

cor.test(plastic_approx, PP_reference$INTENSITY, method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  plastic_approx and PP_reference$INTENSITY
## t = 4.0798, df = 969, p-value = 4.879e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.0675895 0.1912978
## sample estimates:
##       cor 
## 0.1299494

PS_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PS")

cor.test(plastic_approx, PS_reference$INTENSITY, method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  plastic_approx and PS_reference$INTENSITY
## t = -6.3449, df = 969, p-value = 3.41e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2593757 -0.1385508
## sample estimates:
##        cor 
## -0.1997223

PSA_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PSA")

cor.test(plastic_approx, PSA_reference$INTENSITY, method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  plastic_approx and PSA_reference$INTENSITY
## t = -4.6472, df = 969, p-value = 3.829e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.20862802 -0.08553572
## sample estimates:
##        cor 
## -0.1476536

PVC_reference <- dplyr::filter(plastic_ref_lib, SAMPLE == "PVC")

cor.test(plastic_approx, PVC_reference$INTENSITY, method = "pearson")

## 
##  Pearson's product-moment correlation
## 
## data:  plastic_approx and PVC_reference$INTENSITY
## t = 10.367, df = 969, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2582033 0.3715099
## sample estimates:
##       cor 
## 0.3159829

cor(plastic_approx, PVC_reference$INTENSITY, method = "pearson")

## [1] 0.3159829

plot(freq_index, PVC_reference$INTENSITY, type = "l")+
lines(freq_index, plastic_approx, col = "red")

## integer(0)

#Same value as cor.test

#This is what they used for their hit index in Choy et al. 2019
#PVC is the highest correlation by far

#This version is deprecated as it takes all the spectra from that study and not just the plastic reference.
#poly_labs <- as.character(unique(ref_lib$poly_lab))

poly_labs <-as.character(unique(plastic_ref_lib$poly_lab))

cor_table <- as.data.frame(matrix(ncol = length(poly_labs), nrow = 1, dimnames = list(NULL, poly_labs)))

#for(name in poly_labs){
#  temp_filter <- filter(plastic_ref_lib, poly_lab == name)
#  cor_table[,name] <- cor(plastic_approx, temp_filter$INTENSITY, method = "pearson")
  
#}

#That for loop did not work

plastic_lab <- data.frame()

for(i in 1:length(poly_labs)){
  temp_filter <- dplyr::filter(plastic_ref_lib, poly_lab == poly_labs[i])
  cor_table[,i] <- round(cor(plastic_approx, temp_filter$INTENSITY, method = "pearson"), digits = 3)
  
 for (d in 1:nrow(cor_table)){
  plastic_lab[d,1]<-colnames(cor_table[which.max(cor_table[d,])])
  
  }
}


ggtexttable(cor_table, theme = ttheme(base_size = 7.5)) %>% table_cell_bg(row = 2, column = 15, fill = "red")

setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/10_24_19_spectral_data/")
plastic_test_files <- list.files(path = "/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/10_24_19_spectral_data/", pattern = "*.txt")

file_numbers <- seq(plastic_test_files)

plastic_test_spc_table <- matrix()

freq_index <- seq(780, 1750, by = 1)

all_sample_particle_WV <- as.data.frame(freq_index)

#start of a larger raman data import function need to uncomment for it to work

#Raman_large_import <- function(filepath){
  #freq_index <- seq(780, 1750, by = 1)

#all_sample_particle_WV <- as.data.frame(freq_index)
#for loop over .txt files and create a df
for(filename in plastic_test_files){
  temp_csv <- read.delim(file = filename, header = F, sep = "\t")
  colnames(temp_csv) <- c("Wavelength", "Counts")
  temp_csv_matrix <- as.matrix(temp_csv)
  approx_test_fun <- approxfun(x = temp_csv_matrix[,'Wavelength'], y = temp_csv_matrix[,'Counts'])
  new_counts <- approx_test_fun(freq_index)
  all_sample_particle_WV[filename] <- new_counts
}

all_sample_particle_WV_t <- t(all_sample_particle_WV)

colnames(all_sample_particle_WV_t) <- all_sample_particle_WV_t[1,]

all_sample_particle_WV_t <- all_sample_particle_WV_t[-1,]

potplastic_filename <- rownames(all_sample_particle_WV_t)

potp_hyper <- as.hyperSpec(all_sample_particle_WV_t, data = data.frame(potplastic_filename))

#This version of as.hyper works and keeps the filenames for each raman spectra

#Baselines
potp_baselines <-  spc.fit.poly.below(potp_hyper, poly.order = 6)
#Plotting baseline corrections
plot(potp_hyper-potp_baselines)
title(xlab = "Wavelength",ylab = "Arbitrary unit of excitation")

potp_hyper_corrected <- potp_hyper-potp_baselines

#Standard Normal variate normalization
potp_hyper_corrected@data[["spc"]] <- standardNormalVariate(potp_hyper_corrected@data[["spc"]])

#min-max scaling
potp_hyper_corrected@data[["spc"]] <- (potp_hyper_corrected@data[["spc"]] - min(potp_hyper_corrected@data[["spc"]])) / (max(potp_hyper_corrected@data[["spc"]])-min(potp_hyper_corrected@data[["spc"]]))
#}

plot(potp_hyper_corrected, col = rainbow(39),title.args = list(xlab = "Wavelength",ylab = "Arbitrary unit of Intensity"))

#View(t(all_sample_particle_WV))

This chunk now imports and loops over all .txt files which will correspond to individual particles and transform them into a matrix for proper raman processing. Now I need to make them into a hyperspec obj and do the proper standard normal variate, baseline correction, and min-max standardization.

#poly_labs <- as.character(unique(ref_lib$poly_lab))

#poly_labs <- poly_labs[-15]
#poly_labs <-as.character(unique(plastic_ref_lib$poly_lab))

poly_labs <-as.character(unique(plastic_ref_lib$poly_lab))

cor_table_manyplastic <- as.data.frame(matrix(ncol = length(poly_labs), nrow = nrow(potp_hyper_corrected@data[["spc"]]), dimnames = list(NULL, poly_labs)))

for(row in 1:nrow(potp_hyper_corrected@data[["spc"]])){
  for(i in seq_along(poly_labs)){
  temp_filter <- dplyr::filter(plastic_ref_lib, poly_lab == poly_labs[i])
  cor_table_manyplastic[row,i] <- round(cor(potp_hyper_corrected@data[["spc"]][row,], temp_filter$INTENSITY, method = "pearson"), digits = 3)
}
}

plastic_lab <- as.data.frame(matrix(nrow = nrow(cor_table_manyplastic), ncol = 2))

colnames(plastic_lab)  <- c("Hit_Index_Value","Polymer_ID")

rownames(plastic_lab) <- plastic_test_files

for (d in 1:nrow(cor_table_manyplastic)){
  plastic_lab[d,1]<-cor_table_manyplastic[d,which.max(cor_table_manyplastic[d,])]
  plastic_lab[d,2]<-colnames(cor_table_manyplastic[which.max(cor_table_manyplastic[d,])])
  
  }

plastic_lab_filt <- plastic_lab %>% 
  rownames_to_column('filename') %>% 
  dplyr::filter(Hit_Index_Value > 0.22) %>% 
  column_to_rownames('filename')

plastic_lab_count <- group_by(plastic_lab_filt, Polymer_ID) %>% summarise(n())

plastic_lab_count

## # A tibble: 4 x 2
##   Polymer_ID `n()`
##   <chr>      <int>
## 1 PA            13
## 2 PET            1
## 3 PMMA           1
## 4 POM            3

ggtexttable(plastic_lab_count)

ggtexttable(plastic_lab_filt)

#temp_row <- potp_hyper_corrected@data[["spc"]][row,]

#for(row in 1:nrow(potp_hyper_corrected@data[["spc"]])){
  #print(potp_hyper_corrected@data[["spc"]][row,])
#}

#for(i in 1:length(poly_labs)){
#  temp_filter <- filter(plastic_ref_lib, poly_lab == poly_labs[i])
#  return(temp_filter)
#}

#Does this pipeline hold up to with the new spectral data?
#If so, I need to add in additional reference from openspecy, data which I can download
#Making notes of each step to make it more readable for future reference
setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/Antarctic_Microplastic_Raman_Spectra/6_13_23_Raman_test_runs/")
#Creates a list of all file names within the desired folder 
#Each folder with house an entire samples spectral data
plastic_test_files <- list.files(path = "/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/Antarctic_Microplastic_Raman_Spectra/6_13_23_Raman_test_runs/", pattern = "*.txt")
#Index values to be looped over
file_numbers <- seq(plastic_test_files)
#Blank matrix table to be inserted into an hyperspec object
plastic_test_spc_table <- matrix()
#Desired range of spectral data for identification
#data range collected is much larger, ranging form ~100 - ~2500 wavelength
freq_index <- seq(780, 1750, by = 1)
#Empty dataframe from which to add in each spectra
all_sample_particle_WV <- as.data.frame(freq_index)

#start of a larger raman data import function need to uncomment for it to work

#Raman_large_import <- function(filepath){
  #freq_index <- seq(780, 1750, by = 1)

#all_sample_particle_WV <- as.data.frame(freq_index)

#for loop over .txt files and create a df of spectra
#This loops and adds the smoothed version of the spectra within the wavelength ranges desired, which can be changed at any time by changing the "freq_index" object
for(filename in plastic_test_files){
  temp_csv <- read.delim(file = filename, header = F, sep = "\t")
  colnames(temp_csv) <- c("Wavelength", "Counts")
  temp_csv_matrix <- as.matrix(temp_csv)
  approx_test_fun <- approxfun(x = temp_csv_matrix[,'Wavelength'], y = temp_csv_matrix[,'Counts'])
  new_counts <- approx_test_fun(freq_index)
  all_sample_particle_WV[filename] <- new_counts
}
#Matrix is in the wrong format and needs to by transposed to fit the hyperspec obj
all_sample_particle_WV_t <- t(all_sample_particle_WV)
#Converting the first column into column names
colnames(all_sample_particle_WV_t) <- all_sample_particle_WV_t[1,]

all_sample_particle_WV_t <- all_sample_particle_WV_t[-1,]
#Extracting the filenames or name of specific spectra being analyzed
potplastic_filename <- rownames(all_sample_particle_WV_t)
#Making hyperspec obj with matrix of spectra with filename meta data
potp_hyper <- as.hyperSpec(all_sample_particle_WV_t, data = data.frame(potplastic_filename))

#This version of as.hyper works and keeps the filenames for each raman spectra

#Baseline correction
potp_baselines <-  spc.fit.poly.below(potp_hyper, poly.order = 6)
#Plotting baseline corrections
plot(potp_hyper-potp_baselines)
title(xlab = "Wavelength",ylab = "Arbitrary unit of excitation")

potp_hyper_corrected <- potp_hyper-potp_baselines

#Standard Normal variate normalization
potp_hyper_corrected@data[["spc"]] <- standardNormalVariate(potp_hyper_corrected@data[["spc"]])

#min-max scaling
potp_hyper_corrected@data[["spc"]] <- (potp_hyper_corrected@data[["spc"]] - min(potp_hyper_corrected@data[["spc"]])) / (max(potp_hyper_corrected@data[["spc"]])-min(potp_hyper_corrected@data[["spc"]]))
#}

plot(potp_hyper_corrected, col = rainbow(39),title.args = list(xlab = "Wavelength",ylab = "Arbitrary unit of Intensity"))

#Extracting unique names from reference library
#poly_labs <- as.character(unique(ref_lib$poly_lab))
#remove the last column (why?)
#poly_labs <- poly_labs[-15]

#This previous version took all samples from the study and not just the reference plastic. All of the categorized "." were environmental samples thus necessitating removal. I had a filtered df for plastic reference only so use that df for plastic label names.

poly_labs <-as.character(unique(plastic_ref_lib$poly_lab))
#Create an correlation matrix to fill that are the dimensions of the plastic reference vs the number of spectra collected within a single sample
cor_table_manyplastic <- as.data.frame(matrix(ncol = length(poly_labs), nrow = nrow(potp_hyper_corrected@data[["spc"]]), dimnames = list(NULL, poly_labs)))
#Calculating correlations and filling the DF with the values
for(row in 1:nrow(potp_hyper_corrected@data[["spc"]])){
  for(i in seq_along(poly_labs)){
  temp_filter <- dplyr::filter(plastic_ref_lib, poly_lab == poly_labs[i])
  cor_table_manyplastic[row,i] <- round(cor(potp_hyper_corrected@data[["spc"]][row,], temp_filter$INTENSITY, method = "pearson"), digits = 3)
}
}
#Creating an empty df to hold the identified plastic info in
plastic_lab <- as.data.frame(matrix(nrow = nrow(cor_table_manyplastic), ncol = 2))

colnames(plastic_lab)  <- c("Hit_Index_Value","Polymer_ID")

rownames(plastic_lab) <- plastic_test_files
#Search the correlation matrix for plastic that are the highest correlated to the spectra with the filename (or really particle name).
for (d in 1:nrow(cor_table_manyplastic)){
  plastic_lab[d,1]<-cor_table_manyplastic[d,which.max(cor_table_manyplastic[d,])]
  plastic_lab[d,2]<-colnames(cor_table_manyplastic[which.max(cor_table_manyplastic[d,])])
  
  }
#Filter spectra that don't meet the spectra limit
plastic_lab_filt <- plastic_lab %>% 
  rownames_to_column('filename') %>% 
  dplyr::filter(Hit_Index_Value > 0.22) %>% 
  column_to_rownames('filename')

#Summarise the counts for each polymer - But I need to have a way to add to a df with polymers unidentified in the sample but we have references for.
plastic_lab_count <- group_by(plastic_lab_filt, Polymer_ID) %>% summarise(n())

plastic_lab_count

## # A tibble: 6 x 2
##   Polymer_ID `n()`
##   <chr>      <int>
## 1 PA             5
## 2 PC             1
## 3 PET            2
## 4 PLA            1
## 5 POM            1
## 6 PP             3

ggtexttable(plastic_lab_count)

ggtexttable(plastic_lab_filt)

#Its the summarised counts that I need to import into a new df with each sample and 

#Some Notes From OpenSpecy Metadata and SOP OpenSpecy uses the same hit indexing as I have used in my Raman pipeline. They do the same baseline removal. My cutoff threshold is slightly lower than their threshold of 0.3, whereas due to degredation I’m using a cutoff (0.22) designated from the 2019 paper I adopted this identification process from. That study used degraded fishing tools as their reference to create the cutoff. OpenSpecy have an R shiny App I could use for identification but I believe since their algorithyms are the same I am going to continue writing my script using the Hyperspec Package and data structure to make downstream analysis a bit easier and allows for greater flexibility with what I can do with the data.

The main issue here is you need to analyze each spectra one at a time. My script allows for analysis for an entire samples hundreds of spectra in a single run of the code.

#State of the script I want to make this script have the ability to take in a single spectra for a sample up to thousands of spectra in a single sample and be able to identify each plastic polymer and enumerate them ##Steps I have done: Spectral Preprossessing (smoothing, scaling, baseline correction) Make into a single function Spectral Identification (correlations, hit index, selecting highest correlation) Make into a single function - return a df with the identification ##What is my script missing? Data frame with polymer IDs and total for each polymer with additional environmental data Create data frame from which I can add to as I collect samples with sample name, depth, size fraction, and all the collected information. I need to add to the reference library - the one from OpenSpecy with thousands of different polymer reference spectra I want to see if I can make the plots with ggplot and a hyperspec object…think I can by extracting the SPC data from the “HS” obj I want to see if I can apply the same smoothing function as openspecy does as I am using their reference database…Its not necessary as it makes almost no difference in the estimated coefficients and the approxfun cleans it up enough, although may make some correlations lower than they otherwise would be.

There are some important peaks being missed by not including the 500 nm wavelegnth range. I will change the range of the smoothing process from 750 to 500 and change the upper range to 1900 - 2000 nm.

#If I can import OpenSpecy library and get the same values as they do I will be happy to move onto the next step.
open_spec_reflib <- read.csv("/Users/christophercarnivale/Desktop/Dissertation_data/Microplastic_antarctica/raman_library.csv")

open_spec_reflib_metadata <- read.csv("/Users/christophercarnivale/Desktop/Dissertation_data/Microplastic_antarctica/raman_metadata.csv")

#Need to merge libraries and add information to the metadata csv file for my use

#First change the df to fit that I'm merging
plastic_ref_lib_for_merge <- select(plastic_ref_lib, WAVE, INTENSITY, poly_lab)

plastic_ref_lib_for_merge['sample_name'] <- 1

for(i in seq_along(poly_labs)){
  plastic_ref_lib_for_merge[which(plastic_ref_lib_for_merge$poly_lab == poly_labs[i]),]['sample_name'] <- rep(seq(623, 623+length(poly_labs), by = 1)[i], nrow(plastic_ref_lib_for_merge[which(plastic_ref_lib_for_merge$poly_lab == poly_labs[i]),]))
} 

plastic_ref_lib_for_merge <- select(plastic_ref_lib_for_merge, WAVE, INTENSITY, sample_name, poly_lab) 
colnames(plastic_ref_lib_for_merge) <- colnames(open_spec_reflib)#This now makes the libraries compatible for merger
#Create a single hyperspec object with all of the reference spectra in the "spc" portion of the data.

#Vector of values on with to index the reference library dataframe. I dont need to create a hyperspec object UNLESS I need to change the values of the reference library.
OS_plastic_id_name <- unique(open_spec_reflib$sample_name)

#I don't think I need to create a hyperspec object I just need to convert the library to a form in which can be used in a cor analysis. Cor() needs the same number of observations for both inputs for the comparison.
#This essentially means I need to convert the raw library spectra with the same smoothing and approx patterns. I should finish applying the smoothing function into the script and then apply it to the library.

#This doesn't work as there are too many spectra with similar spectrum identity names
test_id_name <- unique(open_spec_reflib_metadata$spectrum_identity)

#Create an correlation matrix to fill that are the dimensions of the plastic reference vs the number of spectra collected within a single sample
cor_table_manyplastic <- as.data.frame(matrix(ncol = length(OS_plastic_id_name), nrow = nrow(potp_hyper_corrected@data[["spc"]]), dimnames = list(NULL, OS_plastic_id_name)))
#Calculating correlations and filling the DF with the values
for(row in 1:nrow(potp_hyper_corrected@data[["spc"]])){
  for(i in seq_along(OS_plastic_id_name)){
  temp_filter <- dplyr::filter(open_spec_reflib, OS_plastic_id_name == OS_plastic_id_name[i])
  #This is the step where I subset the reference into the same form as the imported data processed prior
  #Now I need to process the original spectra in the same manner that I have 
  cor_table_manyplastic[row,i] <- round(cor(potp_hyper_corrected@data[["spc"]][row,], temp_filter$INTENSITY, method = "pearson"), digits = 3)
}
}

#head(open_spec_reflib_metadata)
#head(open_spec_reflib)
#head(plastic_ref_lib_for_merge)
#str(open_spec_reflib)

#MERGING libraries here...
Merge_test <- rbind(plastic_ref_lib_for_merge, open_spec_reflib)
#This works
#Now I have a df from which I can parse out and use for plasic identification

#Does this pipeline hold up to with the new spectral data?
#If so, I need to add in additional reference from openspecy, data which I can download
#Making notes of each step to make it more readable for future reference
setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/Antarctic_Microplastic_Raman_Spectra/Test_6_28_23/St_3/Less_20/")
#Creates a list of all file names within the desired folder 
#Each folder with house an entire samples spectral data
plastic_test_files <- list.files(path = "/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/Antarctic_Microplastic_Raman_Spectra/Test_6_28_23/St_3/Less_20/", pattern = "*.txt")
#Index values to be looped over
file_numbers <- seq(plastic_test_files)
#Blank matrix table to be inserted into an hyperspec object
plastic_test_spc_table <- matrix()
#Desired range of spectral data for identification
#data range collected is much larger, ranging form ~100 - ~2500 wavelength
freq_index <- seq(500, 1750, by = 0.1)
#Empty dataframe from which to add in each spectra
all_sample_particle_WV <- as.data.frame(freq_index)

#start of a larger raman data import function need to uncomment for it to work

#Raman_large_import <- function(filepath){
  #freq_index <- seq(780, 1750, by = 1)

#all_sample_particle_WV <- as.data.frame(freq_index)

#for loop over .txt files and create a df of spectra
#This loops and adds the smoothed version of the spectra within the wavelength ranges desired, which can be changed at any time by changing the "freq_index" object
for(filename in plastic_test_files){
  temp_csv <- read.delim(file = filename, header = F, sep = "\t")
  colnames(temp_csv) <- c("Wavelength", "Counts")
  temp_csv_matrix <- as.matrix(temp_csv)
  approx_test_fun <- approxfun(x = temp_csv_matrix[,'Wavelength'], y = temp_csv_matrix[,'Counts'], rule = 2)
  new_counts <- approx_test_fun(freq_index)
  new_counts_smoother <- sgolayfilt(new_counts, 3,11, m = 1)
  all_sample_particle_WV[filename] <- new_counts_smoother
}
#Matrix is in the wrong format and needs to by transposed to fit the hyperspec obj
all_sample_particle_WV_t <- t(all_sample_particle_WV)
#Converting the first row into column names
colnames(all_sample_particle_WV_t) <- all_sample_particle_WV_t[1,]

all_sample_particle_WV_t <- all_sample_particle_WV_t[-1,]
#Extracting the filenames or name of specific spectra being analyzed
potplastic_filename <- rownames(all_sample_particle_WV_t)
#Making hyperspec obj with matrix of spectra with filename meta data
potp_hyper <- as.hyperSpec(all_sample_particle_WV_t, data = data.frame(potplastic_filename))

#This version of as.hyper works and keeps the filenames for each raman spectra

#Baseline correction
potp_baselines <-  spc.fit.poly.below(potp_hyper, poly.order = 6)
#Plotting baseline corrections
plot(potp_hyper-potp_baselines)
title(xlab = "Wavelength",ylab = "Arbitrary unit of excitation")

potp_hyper_corrected <- potp_hyper-potp_baselines

#Standard Normal variate normalization
potp_hyper_corrected@data[["spc"]] <- standardNormalVariate(potp_hyper_corrected@data[["spc"]])

#min-max scaling
potp_hyper_corrected@data[["spc"]] <- (potp_hyper_corrected@data[["spc"]] - min(potp_hyper_corrected@data[["spc"]])) / (max(potp_hyper_corrected@data[["spc"]])-min(potp_hyper_corrected@data[["spc"]]))
#}

plot(potp_hyper_corrected, col = rainbow(39),title.args = list(xlab = "Wavelength",ylab = "Arbitrary unit of Intensity"))+legend(500, 1, legend = potplastic_filename,col = rainbow(39))
plot(potp_hyper_corrected)
#Implemented the new smoothing algorithm...now I need to implement this identification with the new library and how that works.

#Extracting unique names from reference library
#poly_labs <- as.character(unique(ref_lib$poly_lab))
#remove the last column (why?)
#poly_labs <- poly_labs[-15]

#This previous version took all samples from the study and not just the reference platic. All of the categorized "." were environmental samples thus necessitating removal. I had a filtered df for plastic reference only so use that df for plastic label names.

poly_labs <-as.character(unique(plastic_ref_lib$poly_lab))
#Create an correlation matrix to fill that are the dimensions of the plastic reference vs the number of spectra collected within a single sample
cor_table_manyplastic <- as.data.frame(matrix(ncol = length(poly_labs), nrow = nrow(potp_hyper_corrected@data[["spc"]]), dimnames = list(NULL, poly_labs)))
#Calculating correlations and filling the DF with the values
for(row in 1:nrow(potp_hyper_corrected@data[["spc"]])){
  for(i in seq_along(poly_labs)){
  temp_filter <- dplyr::filter(plastic_ref_lib, poly_lab == poly_labs[i])
  temp_approx <- approxfun()
  cor_table_manyplastic[row,i] <- round(cor(potp_hyper_corrected@data[["spc"]][row,], temp_filter$INTENSITY, method = "pearson"), digits = 3)
}
}
#Creating an empty df to hold the identified plastic info in
plastic_lab <- as.data.frame(matrix(nrow = nrow(cor_table_manyplastic), ncol = 2))

colnames(plastic_lab)  <- c("Hit_Index_Value","Polymer_ID")

rownames(plastic_lab) <- plastic_test_files
#Search the correlation matrix for plastic that are the highest correlated to the spectra with the filename (or really particle name).
for (d in 1:nrow(cor_table_manyplastic)){
  plastic_lab[d,1]<-cor_table_manyplastic[d,which.max(cor_table_manyplastic[d,])]
  plastic_lab[d,2]<-colnames(cor_table_manyplastic[which.max(cor_table_manyplastic[d,])])
  
  }
#Filter spectra that don't meet the spectra limit
plastic_lab_filt <- plastic_lab %>% 
  rownames_to_column('filename') %>% 
  dplyr::filter(Hit_Index_Value > 0.22) %>% 
  column_to_rownames('filename')

#Summarise the counts for each polymer - But I need to have a way to add to a df with polymers unidentified in the sample but we have references for.
plastic_lab_count <- group_by(plastic_lab_filt, Polymer_ID) %>% summarise(n())

plastic_lab_count

ggtexttable(plastic_lab_count)

ggtexttable(plastic_lab_filt)

#Does this pipeline hold up to with the new spectral data?
#If so, I need to add in additional reference from openspecy, data which I can download
#Making notes of each step to make it more readable for future reference
setwd("/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/Antarctic_Microplastic_Raman_Spectra/7_06_23/Stn_R/Surface/less_20/")
#Creates a list of all file names within the desired folder 
#Each folder with house an entire samples spectral data
plastic_test_files <- list.files(path = "/Users/christophercarnivale/Desktop/Dissertation_data/Raman Spectral Data copy/Antarctic_Microplastic_Raman_Spectra/7_06_23/Stn_R/Surface/less_20/", pattern = "*.txt")
#Index values to be looped over
file_numbers <- seq(plastic_test_files)
#Blank matrix table to be inserted into an hyperspec object
plastic_test_spc_table <- matrix()
#Desired range of spectral data for identification
#data range collected is much larger, ranging form ~100 - ~2500 wavelength
#This frequency index is for the inclusion of the oringinal library I can extend the range when I remove the use of the original library by calling the open_spec_ref_lib df instead of the merge test
freq_index <- seq(780, 1750, by = 0.1)
#Empty dataframe from which to add in each spectra
all_sample_particle_WV <- as.data.frame(freq_index)

#start of a larger raman data import function need to uncomment for it to work

#Raman_large_import <- function(filepath){
  #freq_index <- seq(780, 1750, by = 1)

#all_sample_particle_WV <- as.data.frame(freq_index)

#for loop over .txt files and create a df of spectra
#This loops and adds the smoothed version of the spectra within the wavelength ranges desired, which can be changed at any time by changing the "freq_index" object
for(filename in plastic_test_files){
  temp_csv <- read.delim(file = filename, header = F, sep = "\t")
  colnames(temp_csv) <- c("Wavelength", "Counts")
  temp_csv_matrix <- as.matrix(temp_csv)
  approx_test_fun <- approxfun(x = temp_csv_matrix[,'Wavelength'], y = temp_csv_matrix[,'Counts'], rule = 2)
  new_counts <- approx_test_fun(freq_index)
  new_counts_smoother <- sgolayfilt(new_counts, 3,11)
  all_sample_particle_WV[filename] <- new_counts_smoother
}
#Matrix is in the wrong format and needs to by transposed to fit the hyperspec obj
all_sample_particle_WV_t <- t(all_sample_particle_WV)
#Converting the first column into column names
colnames(all_sample_particle_WV_t) <- all_sample_particle_WV_t[1,]

all_sample_particle_WV_t <- all_sample_particle_WV_t[-1,]
#Extracting the filenames or name of specific spectra being analyzed
potplastic_filename <- rownames(all_sample_particle_WV_t)
#Making hyperspec obj with matrix of spectra with filename meta data
potp_hyper <- as.hyperSpec(all_sample_particle_WV_t, data = data.frame(potplastic_filename))

#This version of as.hyper works and keeps the filenames for each raman spectra

#Baseline correction
potp_baselines <-  spc.fit.poly.below(potp_hyper, poly.order = 6)
#Plotting baseline corrections
plot(potp_hyper-potp_baselines)
title(xlab = "Wavelength",ylab = "Arbitrary unit of excitation")

potp_hyper_corrected <- potp_hyper-potp_baselines

#Standard Normal variate normalization
potp_hyper_corrected@data[["spc"]] <- standardNormalVariate(potp_hyper_corrected@data[["spc"]])

#min-max scaling
potp_hyper_corrected@data[["spc"]] <- (potp_hyper_corrected@data[["spc"]] - min(potp_hyper_corrected@data[["spc"]])) / (max(potp_hyper_corrected@data[["spc"]])-min(potp_hyper_corrected@data[["spc"]]))


plot(potp_hyper_corrected, col = rainbow(40),title.args = list(xlab = "Wavelength",ylab = "Arbitrary unit of Intensity"))
plot(potp_hyper_corrected)
#Implemented the new smoothing algorithm...now I need to implement this identification with the new library and how that works.

#Extracting unique names from reference library
#poly_labs <- as.character(unique(ref_lib$poly_lab))
#remove the last column (why?)
#poly_labs <- poly_labs[-15]

#This previous version took all samples from the study and not just the reference platic. All of the categorized "." were environmental samples thus necessitating removal. I had a filtered df for plastic reference only so use that df for plastic label names.

poly_labs <-as.character(unique(plastic_ref_lib$poly_lab))
#Create an correlation matrix to fill that are the dimensions of the plastic reference vs the number of spectra collected within a single sample
cor_table_manyplastic <- as.data.frame(matrix(ncol = length(OS_plastic_id_name), nrow = nrow(potp_hyper_corrected@data[["spc"]]), dimnames = list(NULL, OS_plastic_id_name)))
#Calculating correlations and filling the DF with the values
for(row in 1:nrow(potp_hyper_corrected@data[["spc"]])){
  for(i in seq_along(OS_plastic_id_name)){
  temp_filter <- dplyr::filter(Merge_test, sample_name == OS_plastic_id_name[i])
  approx_test_fun <- approxfun(x = temp_filter[,'wavenumber'], y = temp_filter[,'intensity'], rule = 2)
  new_counts <- approx_test_fun(freq_index)
  #This frequency index is for the inclusion of the oringinal library I can extend the range when I remove the use of the original library by calling the open_spec_ref_lib df instead of the merge test
  new_counts_smoother <- sgolayfilt(new_counts, 3,11)
  cor_table_manyplastic[row,i] <- round(cor(potp_hyper_corrected@data[["spc"]][row,], new_counts_smoother, method = "pearson"), digits = 3)
}
}

#Debugging... the previous code works for only a small subset of the library. Most of the library fills with NAs
for(row in 1:nrow(potp_hyper_corrected@data[["spc"]])){
  for(i in seq_along(OS_plastic_id_name)){
  temp_filter <- dplyr::filter(Merge_test, sample_name == OS_plastic_id_name[i])
  print(temp_filter)
  approx_test_fun <- approxfun(x = temp_filter[,'wavenumber'], y = temp_filter[,'intensity'], rule = 2)
  new_counts <- approx_test_fun(freq_index)
  #This frequency index is for the inclusion of the oringinal library I can extend the range when I remove the use of the original library by calling the open_spec_ref_lib df instead of the merge test
  new_counts_smoother <- sgolayfilt(new_counts, 3,11)
  cor_table_manyplastic[row,i] <- round(cor(potp_hyper_corrected@data[["spc"]][row,], new_counts_smoother, method = "pearson"), digits = 3)
}
}
#Creating an empty df to hold the identified plastic info in
plastic_lab <- as.data.frame(matrix(nrow = nrow(cor_table_manyplastic), ncol = 2))

colnames(plastic_lab)  <- c("Hit_Index_Value","Polymer_ID")

rownames(plastic_lab) <- plastic_test_files
#Search the correlation matrix for plastic that are the highest correlated to the spectra with the filename (or really particle name).
for (d in 1:nrow(cor_table_manyplastic)){
  plastic_lab[d,1]<-cor_table_manyplastic[d,which.max(cor_table_manyplastic[d,])]
  plastic_lab[d,2]<-colnames(cor_table_manyplastic[which.max(cor_table_manyplastic[d,])])
  
  }
#Filter spectra that don't meet the spectra limit
plastic_lab_filt <- plastic_lab %>% 
  rownames_to_column('filename') %>% 
  dplyr::filter(Hit_Index_Value > 0.2) %>% 
  column_to_rownames('filename')

#Summarise the counts for each polymer - But I need to have a way to add to a df with polymers unidentified in the sample but we have references for.
plastic_lab_count <- group_by(plastic_lab_filt, Polymer_ID) %>% summarise(n())

plastic_lab_count

ggtexttable(plastic_lab_count)

ggtexttable(plastic_lab_filt)

write.csv(plastic_lab_filt, file = "/Users/christophercarnivale/Desktop/Dissertation_data/Microplastic_antarctica/all_plastic_correlations_indexMIN.csv")

write.csv(plastic_lab_count, file = "/Users/christophercarnivale/Desktop/Dissertation_data/Microplastic_antarctica/filtered_plastic_correlations_indexMIN.csv")

derivative_ref_lib <- readRDS("derivative.rds")

derivative_ref_lib[["spectra"]][1:5,1:5]

##    00002e4e3fac430aa1fdfea6e26f85e4 00061dd49cbb71549cf3d530b894ba92
## 1:                               NA                               NA
## 2:                               NA                       0.07754215
## 3:                               NA                       0.07976792
## 4:                               NA                       0.08517078
## 5:                               NA                       0.09375073
##    00087f78d45c571524fce483ef10752e 0008a60c1af45a76ffb91b9cbe1a32e4
## 1:                               NA                               NA
## 2:                               NA                               NA
## 3:                               NA                               NA
## 4:                               NA                               NA
## 5:                               NA                               NA
##    000902ae526db452960c5782843081b4
## 1:                               NA
## 2:                               NA
## 3:                               NA
## 4:                               NA
## 5:                               NA